In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep le...In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep learning has focused on single-layer grating couplers,and the accuracy of multi-layer grating couplers has not yet reached a high level.This paper proposes and demonstrates a novel deep learning network-assisted strategy for inverse design.The network model is based on a multi-layer perceptron(MLP)and incorporates convolutional neural networks(CNNs)and transformers.Through the stacking of multiple layers,it achieves a high-precision design for both multi-layer and single-layer raster couplers with various functionalities.The deep learning network exhibits exceptionally high predictive accuracy,with an average absolute error across the full wavelength range of 1300–1700 nm being only 0.17%,and an even lower predictive absolute error below 0.09%at the specific wavelength of 1550 nm.By combining the deep learning network with the genetic algorithm,we can efficiently design grating couplers that perform different functions.Simulation results indicate that the designed single-wavelength grating couplers achieve coupling efficiencies exceeding 80%at central wavelengths of 1550 nm and 1310 nm.The performance of designed dual-wavelength and broadband grating couplers also reaches high industry standards.Furthermore,the network structure and inverse design method are highly scalable and can be applied not only to multi-layer grating couplers but also directly to the prediction and design of single-layer grating couplers,providing a new perspective for the innovative development of photonic devices.展开更多
In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honey...In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.展开更多
Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a disti...Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a distinct ability to trigger the nonradical pathway in advance oxidation processes(AOPs),promising a stable,rapid and selective degradation of persistent contaminants.However,due to the inherent“black box”nature and limitations of input features,results and conclusions derived from ML may not always be intuitively understood or comprehensively validated.To tackle this challenge,we linked the front-point interpretable analysis approaches with back-point density functional theory(DFT)calculations to form a chained learning strategy for deeper sight into the intrinsic activation mechanism of BCs in AOPs.At the front point,we conducted an easy-to-interpret meta-analysis to validate two strategies for enhancing nonradical pathways by increasing oxygen content and specific surface area(SSA),and prepared oxidized biochar(OBC500)and SSA-increased biochar(SBC900)by controlling pyrolysis conditions and modification methods.Subsequently,experimental results showed that OBC500 and SBC900 had distinct dominant degradation pathways for 1O2 generation and electron transfer,respectively.Finally,at the end point,DFT calculations revealed their active sites and degradation mechanisms.This chained learning strategy elucidates fundamental principles for BC inverse design and showcases the exceptional capacity to integrate computational techniques to accelerate catalyst inverse design.展开更多
Patient-specific finite element analysis(FEA)is a promising tool for noninvasive quantification of cardiac and vascular structural mechanics in vivo.However,inverse material property identification using FEA,which req...Patient-specific finite element analysis(FEA)is a promising tool for noninvasive quantification of cardiac and vascular structural mechanics in vivo.However,inverse material property identification using FEA,which requires iteratively solving nonlinear hyperelasticity problems,is computationally expensive which limits the ability to provide timely patient-specific insights to clinicians.In this study,we present an inverse material parameter identification strategy that integrates deep neural networks(DNNs)with FEA,namely inverse DNN-FEA.In this framework,a DNN encodes the spatial distribution of material parameters and effectively regularizes the inverse solution,which aims to reduce susceptibility to local optima that often arise in heterogeneous nonlinear hyperelastic problems.Consequently,inverse DNN-FEA enables identification of material parameters at the element level.For validation,we applied DNN-FEA to identify four spatially varying passive Holzapfel-Ogden material parameters of the left ventricular myocardium in synthetic benchmark cases with a clinically-derived geometry.To evaluate the benefit of DNN integration,a baseline FEA-only solver implemented in PyTorch was used for comparison.Results demonstrated that DNN-FEA achieved substantially lower average errors in parameter identification compared to FEA(case 1,DNN-FEA:0.37%~2.15%vs.FEA:2.64%~12.91%).The results also demonstrate that the same DNN architecture is capable of identifying a different spatial material property distribution(case 2,DNN-FEA:0.03%~0.60%vs.FEA:0.93%~16.25%).These findings suggest that DNN-FEA provides an accurate framework for inverse identification of heterogeneous myocardial material properties.This approach may facilitate future applications in patient-specific modeling based on in vivo clinical imaging and could be extended to other biomechanical simulation problems.展开更多
This paper investigates the edge-based dynamic event-triggered inverse optimal formation control problem for multiple quadrotor unmanned aerial vehicles(QUAVs) with attitude constraints. To improve communication effic...This paper investigates the edge-based dynamic event-triggered inverse optimal formation control problem for multiple quadrotor unmanned aerial vehicles(QUAVs) with attitude constraints. To improve communication efficiency, an edge-based dynamic event-triggered mechanism is developed for the communication channels between neighboring QUAVs. However, this edge-based dynamic event-triggered communication(DETC) may cause discontinuities in the reference signals. To solve this problem, a distributed estimator is designed for each QUAV to obtain the leader's output signals. Considering the safety of QUAV formation flying, this paper designs a function transformation method that constrains the attitudes of the QUAVs to a strictly safe region. Furthermore, an inverse optimal control strategy is proposed based on the backstepping methodology. This scheme not only minimizes the cost function but also avoids the necessity of solving the Hamilton-Jacobi-Bellman equation. Finally, the stability of the QUAV systems is proven using Lyapunov theory, and the effectiveness of the proposed control method is verified through simulation.展开更多
An upper bound and a lower bound for a0 are given such that aI+B∈M-1 for a>a0 and aI+BM-1 for a≤a0, where B is a nonnegative matrix and satisfies that for any positive constant β,βI+B is a power invariant zero ...An upper bound and a lower bound for a0 are given such that aI+B∈M-1 for a>a0 and aI+BM-1 for a≤a0, where B is a nonnegative matrix and satisfies that for any positive constant β,βI+B is a power invariant zero pattern matrix.展开更多
Finding materials with specific properties is a hot topic in materials science.Traditional materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high co...Finding materials with specific properties is a hot topic in materials science.Traditional materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high costs.With the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new materials.Especially through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired properties.This paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse design.Then,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application scenarios.Finally,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are discussed.The authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.展开更多
Nonperiodic interrupted sampling repeater jamming(ISRJ)against inverse synthetic aperture radar(ISAR)can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation,whi...Nonperiodic interrupted sampling repeater jamming(ISRJ)against inverse synthetic aperture radar(ISAR)can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation,which is obviously dif-ferent from the conventional multi-false-target deception jam-ming.In this paper,a suppression method against this kind of novel jamming is proposed based on inter-pulse energy function and compressed sensing theory.By utilizing the discontinuous property of the jamming in slow time domain,the unjammed pulse is separated using the intra-pulse energy function diffe-rence.Based on this,the two-dimensional orthogonal matching pursuit(2D-OMP)algorithm is proposed.Further,it is proposed to reconstruct the ISAR image with the obtained unjammed pulse sequence.The validity of the proposed method is demon-strated via the Yake-42 plane data simulations.展开更多
We report a robust pillar-layered metal-organic framework,Zn‑tfbdc‑dabco(tfbdc:tetrafluoroterephthal-ate,dabco:1,4-diazabicyclo[2.2.2]octane),featuring the fluorinated pore environment,for the preferential binding of ...We report a robust pillar-layered metal-organic framework,Zn‑tfbdc‑dabco(tfbdc:tetrafluoroterephthal-ate,dabco:1,4-diazabicyclo[2.2.2]octane),featuring the fluorinated pore environment,for the preferential binding of propane over propylene and thus highly inverse selective separation of propane/propylene mixture.The inverse propane-selective performance of Zn‑tfbdc‑dabco for the propane/propylene separation was validated by single-component gas adsorption isotherms,isosteric enthalpy of adsorption calculations,ideal adsorbed solution theory calculations,along with the breakthrough experiment.The customized fluorinated networks served as a propane-trap to form more interactions with the exposed hydrogen atoms of propane,as unveiled by the simulation studies at the molecular level.With the advantage of inverse propane-selective adsorption behavior,high adsorption capacity,good cycling stability,and low isosteric enthalpy of adsorption,Zn‑tfbdc‑dabco can be a promising candidate adsorbent for the challenging propane/propylene separation to realize one-step purification of the target propylene substance.展开更多
In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by re...In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.展开更多
Serial-parallel manipulators are of great interest to academic community in recent years,especially those composed of classical parallel mechanisms.There have been many studies around 2(3RPS)and 2(3SPR)S-PMs,but unfor...Serial-parallel manipulators are of great interest to academic community in recent years,especially those composed of classical parallel mechanisms.There have been many studies around 2(3RPS)and 2(3SPR)S-PMs,but unfortunately their inverse kinematics have not yet been resolved.This paper discovers that the unknown kinematic parameters of middle platform are responsible for the unresolvable of inverse kinematics,meanwhile the unknown kinematic parameters of middle platform also have huge coupling relationships.Therefore,to break through this challenges,the huge coupling relationships are decoupled layer by layer,the kinematic parameters of middle platform are solved by combining Sylvester's elimination method,and the inverse displacements of 2(3RPS)and 2(3SPR)S-PMs are obtained subsequently.This paper not only solves the inverse kinematics of classical 2(3RPS)and 2(3SPR)S-PMs,but also reveals the essence of the inverse kinematics of general(3-DOF)+(3-DOF)6-DOF S-PMs and proposes a corresponding solution.展开更多
The weighted Drazin invertibility of rectangular matrixs over an arbitrary ring are studied.Some equivalent conditions and Characterizations are given for existence of the weighted Drazin inverse of a rectangular matr...The weighted Drazin invertibility of rectangular matrixs over an arbitrary ring are studied.Some equivalent conditions and Characterizations are given for existence of the weighted Drazin inverse of a rectangular matrix over an arbitrary ring.Moreover,the weighted Drazin inverse of a rectangular matrices product PAQ can be characterized and computed.This generalizes results obtained for the Drazin inverse of such product of square matrices.The results also apply to morphisms in(additive)categories.展开更多
The discovery of advanced materials is a cornerstone of human technological development and progress.The structures of materials and their corresponding properties are essentially the result of a complex interplay of ...The discovery of advanced materials is a cornerstone of human technological development and progress.The structures of materials and their corresponding properties are essentially the result of a complex interplay of multiple degrees of freedom such as lattice,charge,spin,symmetry,and topology.This poses significant challenges for the inverse design methods of materials.Humans have long explored new materials through numerous experiments and proposed corresponding theoretical systems to predict new material properties and structures.With the improvement of computational power,researchers have gradually developed various electronic-structure calculation methods,such as the density functional theory and high-throughput computational methods.Recently,the rapid development of artificial intelligence(AI)technology in computer science has enabled the effective characterization of the implicit association between material properties and structures,thus forming an efficient paradigm for the inverse design of functional materials.Significant progress has been achieved in the inverse design of materials based on generative and discriminative models,attracting widespread interest from researchers.Considering this rapid technological progress,in this survey,we examine the latest advancements in AI-driven inverse design of materials by introducing the background,key findings,and mainstream technological development routes.In addition,we summarize the remaining challenges for future directions.This survey provides the latest overview of AI-driven inverse design of materials,which can serve as a useful resource for researchers.展开更多
Reverse design of highly GeO2-doped silica optical fibers with broadband and flat dispersion profiles is proposed using a neural network(NN) combined with a particle swarm optimization(PSO) algorithm.Firstly,the NN mo...Reverse design of highly GeO2-doped silica optical fibers with broadband and flat dispersion profiles is proposed using a neural network(NN) combined with a particle swarm optimization(PSO) algorithm.Firstly,the NN model designed to predict optical fiber dispersion is trained with an appropriate choice of hyperparameters,achieving a root mean square error(RMSE) of 9.47×10-7on the test dataset,with a determination coefficient(R2) of 0.999.Secondly,the NN is combined with the PSO algorithm for the inverse design of dispersion-flattened optical fibers.To expand the search space and avoid particles becoming trapped in local optimal solutions,the PSO algorithm incorporates adaptive inertia weight updating and a simulated annealing algorithm.Finally,by using a suitable fitness function,the designed fibers exhibit flat group velocity dispersion(GVD) profiles at 1 400—2 400 nm,where the GVD fluctuations and minimum absolute GVD values are below 18 ps·nm-1·km-1and 7 ps·nm-1·km-1,respectively.展开更多
Terahertz(THz)metamaterials,with their exceptional ability to precisely manipulate the phase,amplitude,polarization and orbital angular momentum(OAM)of electromagnetic waves,have demonstrated significant application p...Terahertz(THz)metamaterials,with their exceptional ability to precisely manipulate the phase,amplitude,polarization and orbital angular momentum(OAM)of electromagnetic waves,have demonstrated significant application potential across a wide range of fields.However,traditional design methodologies often rely on extensive parameter sweeps,making it challenging to address the increasingly complex and diverse application requirements.Recently,the integration of artificial intelligence(AI)techniques,particularly deep learning and optimization algorithms,has introduced new approaches for the design of THz metamaterials.This paper reviews the fundamental principles of THz metamaterials and their intelligent design methodologies,with a particular focus on the advancements in AI-driven inverse design of THz metamaterials.The AI-driven inverse design process allows for the creation of THz metamaterials with desired properties by working backward from the unit structures and array configurations of THz metamaterials,thereby accelerating the design process and reducing both computational resources and time.It examines the critical role of AI in improving both the functionality and design efficiency of THz metamaterials.Finally,we outline future research directions and technological challenges,with the goal of providing valuable insights and guidance for ongoing and future investigations.展开更多
We introduce and study a new kind of generalized inverses named w-(b,c)-core inverses,which is a generalization of the(b,c)-core inverse.An example is given to show that w-(b,c)-core inverses need not be(b,c)-core inv...We introduce and study a new kind of generalized inverses named w-(b,c)-core inverses,which is a generalization of the(b,c)-core inverse.An example is given to show that w-(b,c)-core inverses need not be(b,c)-core inverses.In addition,the dual version of the w-(b,c)-core inverse is studied.Some results on(b,c)-core inverses and e-(b,c)-core inverses are unifed and generalized.展开更多
Flowfield inverse design can obtain the desired flow and contour with high design efficiency,short design cycle,and small modification need.In this study,the Euler equations are formulated in the stream-function coord...Flowfield inverse design can obtain the desired flow and contour with high design efficiency,short design cycle,and small modification need.In this study,the Euler equations are formulated in the stream-function coordinates and combined with the given boundary conditions to derive a gridless space-marching method for the inverse design of subsonic,transonic,and supersonic flowfields.Designers can prescribe the flow parameters along the reference streamline to design flowfields and aerodynamic contours.The method is validated by the theoretical transonic solution,computational fluid dynamics,and experimental data,respectively.The method supports the fabrication of a Mach 2.0 single expansion tunnel.The calibration data agree well with the prescribed pressure distribution.The method is successfully applied to inverse design of contractions,nozzles,and asymmetric channels.Compared to classical analytic contractions,the contractions designed by the space-marching method provide a more accurate transonic flow.Compared to the classical Sivells’nozzle,the nozzle designed by the space-marching method provides a smaller workload,a more flexible velocity distribution,a 20%reduction in length,and an equally uniform flow.Additionally,the space-marching method is applied to design the asymmetric channels under various Mach numbers.These asymmetric channels perfectly eliminate Mach waves,achieving the shock-free flow turning and high flow uniformity.These results validate the feasibility of the space-marching method,making it a good candidate for the inverse design of subsonic,transonic,and supersonic internal flowfields and aerodynamic contours.展开更多
The integrated waveguide polarizer is essential for photonic integrated circuits,and various designs of waveguide polarizers have been developed.As the demand for dense photonic integration increases rapidly,new strat...The integrated waveguide polarizer is essential for photonic integrated circuits,and various designs of waveguide polarizers have been developed.As the demand for dense photonic integration increases rapidly,new strategies to minimize the device size are needed.In this paper,we have inversely designed an integrated transverse electric pass(TE-pass)polarizer with a footprint of 2.88μm×2.88μm,which is the smallest footprint ever achieved.A direct binary search algorithm is used to inversely design the device for maximizing the transverse electric(TE)transmission while minimizing transverse magnetic(TM)transmission.Finally,the inverse-designed device provides an average insertion loss of 0.99 dB and an average extinction ratio of 33 dB over a wavelength range of 100 nm.展开更多
Liquid crystal elastomers(LCEs)are advanced materials characterized by their rubber-like hyperelasticity and liquid crystal phase transitions,offering exceptional mechanical properties.The development of smart mechani...Liquid crystal elastomers(LCEs)are advanced materials characterized by their rubber-like hyperelasticity and liquid crystal phase transitions,offering exceptional mechanical properties.The development of smart mechanical metamaterials(SMMs)from LCEs expands the potential for controlling mechanical responses and achieving nonlinear behaviors not possible with traditional metamaterials.However,the challenge lies in managing the interplay between nonlinear material responses and structural complexity,making the inverse design of LCE-based SMMs exceptionally demanding.In this paper,we introduce a design framework for LCE smart mechanical metamaterials that leverages neural networks and evolution strategies(ES)to optimize designs with nonlinear mechanical responses.Our approach involves constructing a flexible,unit-cell-based metamaterial model that integrates the soft elastic behavior and thermo-mechanical coupling of LCEs.The combination of microscopic liquid crystal molecule rotation and macroscopic block rotation enables highly tunable and nonlinear mechanical behaviors,of which the precise inverse design of stress-stretch responses is obtained via neural networks combined with ES.In addition,stimuli responses in the liquid crystal elastomers enable real-time adaptability and achieve tailored stress plateaus that are not possible with traditional metamaterials.Our findings provide new pathways in the design and optimization of advanced materials in flexible electronic devices,intelligent actuators,and systems for energy absorption and dissipation.展开更多
Research on detonation has traditionally focused on forward solutions,with limited attention to inverse design methods,which has significantly hindered the development of detonation engines.In this paper,the Method of...Research on detonation has traditionally focused on forward solutions,with limited attention to inverse design methods,which has significantly hindered the development of detonation engines.In this paper,the Method of characteristics for Curved-Detonation(MOCD)is proposed to enable the inverse design of detonation waves.MOCD is based on the Method of Curved-shock Characteristics(MOCC)and integrates higher-order aerodynamic parameters from Curved Detonation Equations(CDE),allowing the calculation of the wedge angle given specific wave angle.The effectiveness of MOCD is validated using both oblique and curved detonation waves with single-step and detailed chemical reactions.Various applications demonstrate the ability to meet the inverse design requirements of detonation engines.For example,inverse design for given wave angles can optimize engine thrust and prevent Mach reflections.Additionally,inverse design schemes tailored to incoming flow conditions,such as varying Mach numbers and equivalence ratios,enhance the feasibility of detonation engines.Applying the method to given post-wave aerodynamic parameters enables more precise engine design,which is crucial for improving propulsion performance and effective thermal protection.In summary,the advantages of MOCD include not only performing a fast solution of the detonation flow field,but also allowing the inverse design of the detonation wave.展开更多
基金sponsored by the National Key Scientific Instrument and Equipment Development Projects of China(Grant No.62027823)the National Natural Science Foun-dation of China(Grant No.61775048).
文摘In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep learning has focused on single-layer grating couplers,and the accuracy of multi-layer grating couplers has not yet reached a high level.This paper proposes and demonstrates a novel deep learning network-assisted strategy for inverse design.The network model is based on a multi-layer perceptron(MLP)and incorporates convolutional neural networks(CNNs)and transformers.Through the stacking of multiple layers,it achieves a high-precision design for both multi-layer and single-layer raster couplers with various functionalities.The deep learning network exhibits exceptionally high predictive accuracy,with an average absolute error across the full wavelength range of 1300–1700 nm being only 0.17%,and an even lower predictive absolute error below 0.09%at the specific wavelength of 1550 nm.By combining the deep learning network with the genetic algorithm,we can efficiently design grating couplers that perform different functions.Simulation results indicate that the designed single-wavelength grating couplers achieve coupling efficiencies exceeding 80%at central wavelengths of 1550 nm and 1310 nm.The performance of designed dual-wavelength and broadband grating couplers also reaches high industry standards.Furthermore,the network structure and inverse design method are highly scalable and can be applied not only to multi-layer grating couplers but also directly to the prediction and design of single-layer grating couplers,providing a new perspective for the innovative development of photonic devices.
基金the financial supports from National Key R&D Program for Young Scientists of China(Grant No.2022YFC3080900)National Natural Science Foundation of China(Grant No.52374181)+1 种基金BIT Research and Innovation Promoting Project(Grant No.2024YCXZ017)supported by Science and Technology Innovation Program of Beijing institute of technology under Grant No.2022CX01025。
文摘In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.
基金supported by Project of National and Local Joint Engineering Research Center for Biomass Energy Development and Utilization(Harbin Institute of Technology,No.2021A004).
文摘Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a distinct ability to trigger the nonradical pathway in advance oxidation processes(AOPs),promising a stable,rapid and selective degradation of persistent contaminants.However,due to the inherent“black box”nature and limitations of input features,results and conclusions derived from ML may not always be intuitively understood or comprehensively validated.To tackle this challenge,we linked the front-point interpretable analysis approaches with back-point density functional theory(DFT)calculations to form a chained learning strategy for deeper sight into the intrinsic activation mechanism of BCs in AOPs.At the front point,we conducted an easy-to-interpret meta-analysis to validate two strategies for enhancing nonradical pathways by increasing oxygen content and specific surface area(SSA),and prepared oxidized biochar(OBC500)and SSA-increased biochar(SBC900)by controlling pyrolysis conditions and modification methods.Subsequently,experimental results showed that OBC500 and SBC900 had distinct dominant degradation pathways for 1O2 generation and electron transfer,respectively.Finally,at the end point,DFT calculations revealed their active sites and degradation mechanisms.This chained learning strategy elucidates fundamental principles for BC inverse design and showcases the exceptional capacity to integrate computational techniques to accelerate catalyst inverse design.
基金supported in part by the National Science Foundation under GrantsDMS 2436630 and 2436629.
文摘Patient-specific finite element analysis(FEA)is a promising tool for noninvasive quantification of cardiac and vascular structural mechanics in vivo.However,inverse material property identification using FEA,which requires iteratively solving nonlinear hyperelasticity problems,is computationally expensive which limits the ability to provide timely patient-specific insights to clinicians.In this study,we present an inverse material parameter identification strategy that integrates deep neural networks(DNNs)with FEA,namely inverse DNN-FEA.In this framework,a DNN encodes the spatial distribution of material parameters and effectively regularizes the inverse solution,which aims to reduce susceptibility to local optima that often arise in heterogeneous nonlinear hyperelastic problems.Consequently,inverse DNN-FEA enables identification of material parameters at the element level.For validation,we applied DNN-FEA to identify four spatially varying passive Holzapfel-Ogden material parameters of the left ventricular myocardium in synthetic benchmark cases with a clinically-derived geometry.To evaluate the benefit of DNN integration,a baseline FEA-only solver implemented in PyTorch was used for comparison.Results demonstrated that DNN-FEA achieved substantially lower average errors in parameter identification compared to FEA(case 1,DNN-FEA:0.37%~2.15%vs.FEA:2.64%~12.91%).The results also demonstrate that the same DNN architecture is capable of identifying a different spatial material property distribution(case 2,DNN-FEA:0.03%~0.60%vs.FEA:0.93%~16.25%).These findings suggest that DNN-FEA provides an accurate framework for inverse identification of heterogeneous myocardial material properties.This approach may facilitate future applications in patient-specific modeling based on in vivo clinical imaging and could be extended to other biomechanical simulation problems.
基金supported by the National Natural Science Foundation of China (Grant Nos.62573134,62473100,62433018)the Guangdong Basic and Applied Basic Research Foundation(Grant Nos.2025A1515060017,2025A1515011436,2025B1515020065,2025A1515011789)the Guangzhou Basic and Applied Basic Research Project (Grant No.2025A04J3534)。
文摘This paper investigates the edge-based dynamic event-triggered inverse optimal formation control problem for multiple quadrotor unmanned aerial vehicles(QUAVs) with attitude constraints. To improve communication efficiency, an edge-based dynamic event-triggered mechanism is developed for the communication channels between neighboring QUAVs. However, this edge-based dynamic event-triggered communication(DETC) may cause discontinuities in the reference signals. To solve this problem, a distributed estimator is designed for each QUAV to obtain the leader's output signals. Considering the safety of QUAV formation flying, this paper designs a function transformation method that constrains the attitudes of the QUAVs to a strictly safe region. Furthermore, an inverse optimal control strategy is proposed based on the backstepping methodology. This scheme not only minimizes the cost function but also avoids the necessity of solving the Hamilton-Jacobi-Bellman equation. Finally, the stability of the QUAV systems is proven using Lyapunov theory, and the effectiveness of the proposed control method is verified through simulation.
基金This project is supported by Science and Art Foundation of Central South University of Technology.
文摘An upper bound and a lower bound for a0 are given such that aI+B∈M-1 for a>a0 and aI+BM-1 for a≤a0, where B is a nonnegative matrix and satisfies that for any positive constant β,βI+B is a power invariant zero pattern matrix.
基金funded by theNationalNatural Science Foundation of China(52061020)Major Science and Technology Projects in Yunnan Province(202302AG050009)Yunnan Fundamental Research Projects(202301AV070003).
文摘Finding materials with specific properties is a hot topic in materials science.Traditional materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high costs.With the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new materials.Especially through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired properties.This paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse design.Then,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application scenarios.Finally,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are discussed.The authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.
基金supported by the National Natural Science Foundation of China(62001481,61890542,62071475)the Natural Science Foundation of Hunan Province(2022JJ40561)the Research Program of National University of Defense Technology(ZK22-46).
文摘Nonperiodic interrupted sampling repeater jamming(ISRJ)against inverse synthetic aperture radar(ISAR)can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation,which is obviously dif-ferent from the conventional multi-false-target deception jam-ming.In this paper,a suppression method against this kind of novel jamming is proposed based on inter-pulse energy function and compressed sensing theory.By utilizing the discontinuous property of the jamming in slow time domain,the unjammed pulse is separated using the intra-pulse energy function diffe-rence.Based on this,the two-dimensional orthogonal matching pursuit(2D-OMP)algorithm is proposed.Further,it is proposed to reconstruct the ISAR image with the obtained unjammed pulse sequence.The validity of the proposed method is demon-strated via the Yake-42 plane data simulations.
文摘We report a robust pillar-layered metal-organic framework,Zn‑tfbdc‑dabco(tfbdc:tetrafluoroterephthal-ate,dabco:1,4-diazabicyclo[2.2.2]octane),featuring the fluorinated pore environment,for the preferential binding of propane over propylene and thus highly inverse selective separation of propane/propylene mixture.The inverse propane-selective performance of Zn‑tfbdc‑dabco for the propane/propylene separation was validated by single-component gas adsorption isotherms,isosteric enthalpy of adsorption calculations,ideal adsorbed solution theory calculations,along with the breakthrough experiment.The customized fluorinated networks served as a propane-trap to form more interactions with the exposed hydrogen atoms of propane,as unveiled by the simulation studies at the molecular level.With the advantage of inverse propane-selective adsorption behavior,high adsorption capacity,good cycling stability,and low isosteric enthalpy of adsorption,Zn‑tfbdc‑dabco can be a promising candidate adsorbent for the challenging propane/propylene separation to realize one-step purification of the target propylene substance.
文摘In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.
基金Supported by National Natural Science Foundation of China(Grant No.52275033)National Natural Science Youth Foundation of China(Grant No.52205033)Hebei Provincial Natural Science Foundation of China(Grant No.E2021203019)。
文摘Serial-parallel manipulators are of great interest to academic community in recent years,especially those composed of classical parallel mechanisms.There have been many studies around 2(3RPS)and 2(3SPR)S-PMs,but unfortunately their inverse kinematics have not yet been resolved.This paper discovers that the unknown kinematic parameters of middle platform are responsible for the unresolvable of inverse kinematics,meanwhile the unknown kinematic parameters of middle platform also have huge coupling relationships.Therefore,to break through this challenges,the huge coupling relationships are decoupled layer by layer,the kinematic parameters of middle platform are solved by combining Sylvester's elimination method,and the inverse displacements of 2(3RPS)and 2(3SPR)S-PMs are obtained subsequently.This paper not only solves the inverse kinematics of classical 2(3RPS)and 2(3SPR)S-PMs,but also reveals the essence of the inverse kinematics of general(3-DOF)+(3-DOF)6-DOF S-PMs and proposes a corresponding solution.
文摘The weighted Drazin invertibility of rectangular matrixs over an arbitrary ring are studied.Some equivalent conditions and Characterizations are given for existence of the weighted Drazin inverse of a rectangular matrix over an arbitrary ring.Moreover,the weighted Drazin inverse of a rectangular matrices product PAQ can be characterized and computed.This generalizes results obtained for the Drazin inverse of such product of square matrices.The results also apply to morphisms in(additive)categories.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.62476278,12434009,and 12204533)supported by the National Key R&D Program of China(Grant No.2024YFA1408601)the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0302402)。
文摘The discovery of advanced materials is a cornerstone of human technological development and progress.The structures of materials and their corresponding properties are essentially the result of a complex interplay of multiple degrees of freedom such as lattice,charge,spin,symmetry,and topology.This poses significant challenges for the inverse design methods of materials.Humans have long explored new materials through numerous experiments and proposed corresponding theoretical systems to predict new material properties and structures.With the improvement of computational power,researchers have gradually developed various electronic-structure calculation methods,such as the density functional theory and high-throughput computational methods.Recently,the rapid development of artificial intelligence(AI)technology in computer science has enabled the effective characterization of the implicit association between material properties and structures,thus forming an efficient paradigm for the inverse design of functional materials.Significant progress has been achieved in the inverse design of materials based on generative and discriminative models,attracting widespread interest from researchers.Considering this rapid technological progress,in this survey,we examine the latest advancements in AI-driven inverse design of materials by introducing the background,key findings,and mainstream technological development routes.In addition,we summarize the remaining challenges for future directions.This survey provides the latest overview of AI-driven inverse design of materials,which can serve as a useful resource for researchers.
基金supported by the Fundamental Research Funds for the Central Universities (No.2024JBZY021)the National Natural Science Foundation of China (No.61575018)。
文摘Reverse design of highly GeO2-doped silica optical fibers with broadband and flat dispersion profiles is proposed using a neural network(NN) combined with a particle swarm optimization(PSO) algorithm.Firstly,the NN model designed to predict optical fiber dispersion is trained with an appropriate choice of hyperparameters,achieving a root mean square error(RMSE) of 9.47×10-7on the test dataset,with a determination coefficient(R2) of 0.999.Secondly,the NN is combined with the PSO algorithm for the inverse design of dispersion-flattened optical fibers.To expand the search space and avoid particles becoming trapped in local optimal solutions,the PSO algorithm incorporates adaptive inertia weight updating and a simulated annealing algorithm.Finally,by using a suitable fitness function,the designed fibers exhibit flat group velocity dispersion(GVD) profiles at 1 400—2 400 nm,where the GVD fluctuations and minimum absolute GVD values are below 18 ps·nm-1·km-1and 7 ps·nm-1·km-1,respectively.
基金supported by the National Key R and D Program of China(No.2022YFF0604801)the National Natural Science Foundation of China(Nos.62271056,62171186,62201037)+3 种基金the Technology Innovation Center of Infrared Remote Sensing Metrology Technology of State Administration for Market Regulation(No.AKYKF2423)the Beijing Natural Science Foundation of China-Haidian Original Innovation Joint Fund(No.L222042)the Open Research Fund of State Key Laboratory of Millimeter Waves(No.K202326)the 111 Project of China(No.B14010).
文摘Terahertz(THz)metamaterials,with their exceptional ability to precisely manipulate the phase,amplitude,polarization and orbital angular momentum(OAM)of electromagnetic waves,have demonstrated significant application potential across a wide range of fields.However,traditional design methodologies often rely on extensive parameter sweeps,making it challenging to address the increasingly complex and diverse application requirements.Recently,the integration of artificial intelligence(AI)techniques,particularly deep learning and optimization algorithms,has introduced new approaches for the design of THz metamaterials.This paper reviews the fundamental principles of THz metamaterials and their intelligent design methodologies,with a particular focus on the advancements in AI-driven inverse design of THz metamaterials.The AI-driven inverse design process allows for the creation of THz metamaterials with desired properties by working backward from the unit structures and array configurations of THz metamaterials,thereby accelerating the design process and reducing both computational resources and time.It examines the critical role of AI in improving both the functionality and design efficiency of THz metamaterials.Finally,we outline future research directions and technological challenges,with the goal of providing valuable insights and guidance for ongoing and future investigations.
基金pported by National Natural Science Foundation of China(Grant No.12161049).
文摘We introduce and study a new kind of generalized inverses named w-(b,c)-core inverses,which is a generalization of the(b,c)-core inverse.An example is given to show that w-(b,c)-core inverses need not be(b,c)-core inverses.In addition,the dual version of the w-(b,c)-core inverse is studied.Some results on(b,c)-core inverses and e-(b,c)-core inverses are unifed and generalized.
基金supported by the National Key Research and Development Program of China(No.2019YFA0405300)the National Natural Science Foundation of China(No.12272405).
文摘Flowfield inverse design can obtain the desired flow and contour with high design efficiency,short design cycle,and small modification need.In this study,the Euler equations are formulated in the stream-function coordinates and combined with the given boundary conditions to derive a gridless space-marching method for the inverse design of subsonic,transonic,and supersonic flowfields.Designers can prescribe the flow parameters along the reference streamline to design flowfields and aerodynamic contours.The method is validated by the theoretical transonic solution,computational fluid dynamics,and experimental data,respectively.The method supports the fabrication of a Mach 2.0 single expansion tunnel.The calibration data agree well with the prescribed pressure distribution.The method is successfully applied to inverse design of contractions,nozzles,and asymmetric channels.Compared to classical analytic contractions,the contractions designed by the space-marching method provide a more accurate transonic flow.Compared to the classical Sivells’nozzle,the nozzle designed by the space-marching method provides a smaller workload,a more flexible velocity distribution,a 20%reduction in length,and an equally uniform flow.Additionally,the space-marching method is applied to design the asymmetric channels under various Mach numbers.These asymmetric channels perfectly eliminate Mach waves,achieving the shock-free flow turning and high flow uniformity.These results validate the feasibility of the space-marching method,making it a good candidate for the inverse design of subsonic,transonic,and supersonic internal flowfields and aerodynamic contours.
基金supported by the National Natural Science Foundation of China(Nos.62175076,62105028,62475085)the Natural Science Foundation of Hubei Province of China(Nos.2024AFA016,2024AFB612)the Open Project Program of Hubei Optical Fundamental Research Center.
文摘The integrated waveguide polarizer is essential for photonic integrated circuits,and various designs of waveguide polarizers have been developed.As the demand for dense photonic integration increases rapidly,new strategies to minimize the device size are needed.In this paper,we have inversely designed an integrated transverse electric pass(TE-pass)polarizer with a footprint of 2.88μm×2.88μm,which is the smallest footprint ever achieved.A direct binary search algorithm is used to inversely design the device for maximizing the transverse electric(TE)transmission while minimizing transverse magnetic(TM)transmission.Finally,the inverse-designed device provides an average insertion loss of 0.99 dB and an average extinction ratio of 33 dB over a wavelength range of 100 nm.
基金supported by the National Natural Science Foundation of China(Grant Nos.12322207,12202120 and T2293720/T2293722)the Shenzhen Science and Technology Program,China(Grant No.JCYJ20220531095210022)+1 种基金the Fundamental Research Funds for the Central Universities(Grant No.HIT.OCEF.2022037)financial support by the National Key Research and Development Program of China(Grant No.2023YFB3812500)。
文摘Liquid crystal elastomers(LCEs)are advanced materials characterized by their rubber-like hyperelasticity and liquid crystal phase transitions,offering exceptional mechanical properties.The development of smart mechanical metamaterials(SMMs)from LCEs expands the potential for controlling mechanical responses and achieving nonlinear behaviors not possible with traditional metamaterials.However,the challenge lies in managing the interplay between nonlinear material responses and structural complexity,making the inverse design of LCE-based SMMs exceptionally demanding.In this paper,we introduce a design framework for LCE smart mechanical metamaterials that leverages neural networks and evolution strategies(ES)to optimize designs with nonlinear mechanical responses.Our approach involves constructing a flexible,unit-cell-based metamaterial model that integrates the soft elastic behavior and thermo-mechanical coupling of LCEs.The combination of microscopic liquid crystal molecule rotation and macroscopic block rotation enables highly tunable and nonlinear mechanical behaviors,of which the precise inverse design of stress-stretch responses is obtained via neural networks combined with ES.In addition,stimuli responses in the liquid crystal elastomers enable real-time adaptability and achieve tailored stress plateaus that are not possible with traditional metamaterials.Our findings provide new pathways in the design and optimization of advanced materials in flexible electronic devices,intelligent actuators,and systems for energy absorption and dissipation.
基金the support of the National Natural Science Foundation of China,China(Nos.U20A2069,U21B6003,and 12302389)the Advanced Aero-Power Innovation Workstation,China(No.HKCX2024-01-017)。
文摘Research on detonation has traditionally focused on forward solutions,with limited attention to inverse design methods,which has significantly hindered the development of detonation engines.In this paper,the Method of characteristics for Curved-Detonation(MOCD)is proposed to enable the inverse design of detonation waves.MOCD is based on the Method of Curved-shock Characteristics(MOCC)and integrates higher-order aerodynamic parameters from Curved Detonation Equations(CDE),allowing the calculation of the wedge angle given specific wave angle.The effectiveness of MOCD is validated using both oblique and curved detonation waves with single-step and detailed chemical reactions.Various applications demonstrate the ability to meet the inverse design requirements of detonation engines.For example,inverse design for given wave angles can optimize engine thrust and prevent Mach reflections.Additionally,inverse design schemes tailored to incoming flow conditions,such as varying Mach numbers and equivalence ratios,enhance the feasibility of detonation engines.Applying the method to given post-wave aerodynamic parameters enables more precise engine design,which is crucial for improving propulsion performance and effective thermal protection.In summary,the advantages of MOCD include not only performing a fast solution of the detonation flow field,but also allowing the inverse design of the detonation wave.
