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
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
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
Co-assembling chiral molecules with achiral compounds via non-covalent interactions like areneperfluoroarene(AP) interactions offers an effective approach for fabricating chiral functional materials.Herein,chiral mole...Co-assembling chiral molecules with achiral compounds via non-covalent interactions like areneperfluoroarene(AP) interactions offers an effective approach for fabricating chiral functional materials.Herein,chiral molecules L/D-PF1 and L/D-PF2 with pyrene groups were synthesized and its chiroptical properties upon co-assembly with achiral compound octafluoronaphthalene(OFN) through AP interaction were systemically studied.The co-assembly of L/D-PF1/OFN and L/D-PF2/OFN exhibited distinct chiroptical properties such as circular dichroism(CD) and circularly polarized luminescence(CPL) signals.Chirality transfer from the chirality center of L/D-PF1 and L/D-PF2 to the achiral OFN and chiral amplification were successfully achieved.Besides,no significant CPL signal was observed in the self-assembly of L/DPF1 or L/D-PF2 while co-assembly with OFN exhibited obvious CPL amplification induced by AP interaction.Notably,a reversal CD signal and CPL signal could be observed in L/D-PF2/OFN when the molar ratio changed from 1:1 to 1:2 while not found in L/D-PF1/OFN,indicating that that minor structural changes of molecules could cause large changes in assembly.In addition,a series of computational calculations were conducted to verify the AP interaction between L-PF1/L-PF2 and OFN.This work demonstrated that arene-perfluoroarene interaction could drive chiral transfer,chiral amplification and chiral inversion and provided a new method for the preparation of chiroptical materials.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
Subsurface reservoirs commonly exhibit layered structures.Conventional amplitude variation with angle(AVA)inversion,which relies on the Zoeppritz equation and its approximations,often fails to accurately estimate elas...Subsurface reservoirs commonly exhibit layered structures.Conventional amplitude variation with angle(AVA)inversion,which relies on the Zoeppritz equation and its approximations,often fails to accurately estimate elastic parameters because it assumes single-interface models and ignores multiple reflections and transmission losses.To address these limitations,this study proposes a novel prestack time-frequency domain joint inversion method that utilizes the reflection matrix method(RMM)as the forward operator.The RMM accurately simulates wave propagation in layered media,while the joint inversion framework minimizes the misfit between observed and synthetic data in both the time and frequency domains.By incorporating Bayesian theory to optimize the inversion process,the method effectively balances contributions from both time-domain waveforms and frequency-domain spectral information through a weighting factor.Tests on both synthetic data and field data demonstrate that the proposed method outperforms conventional AVA inversion and time-domain waveform inversion in accuracy and robustness.Furthermore,the method demonstrates good robustness against variations in initial models,random noise,and coherent noise interference.This study provides a practical and effective approach for high-precision reservoir characterization,with potential applications in complex layered media.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
Young's modulus and Poisson's ratio are crucial parameters for reservoir characterization and rock brittleness evaluation.Conventional methods often rely on indirect computation or approximations of the Zoeppr...Young's modulus and Poisson's ratio are crucial parameters for reservoir characterization and rock brittleness evaluation.Conventional methods often rely on indirect computation or approximations of the Zoeppritz equations to estimate Young's modulus,which can introduce cumulative errors and reduce the accuracy of inversion results.To address these issues,this paper introduces the analytical solution of the Zoeppritz equation into the inversion process.The equation is re-derived and expressed in terms of Young's modulus,Poisson's ratio,and density.Within the Bayesian framework,we construct an objective function for the joint inversion of PP and PS waves.Traditional gradient-based algorithms often suffer from low precision and the computational complexity.In this study,we address limitations of conventional approaches related to low precision and complicated code by using Circle chaotic mapping,Levy flights,and Gaussian mutation to optimize the quantum particle swarm optimization(QPSO),named improved quantum particle swarm optimization(IQPSO).The IQPSO demonstrates superior global optimization capabilities.We test the proposed inversion method with both synthetic and field data.The test results demonstrate the proposed method's feasibility and effectiveness,indicating an improvement in inversion accuracy over traditional methods.展开更多
Incremental Nonlinear Dynamic Inversion(INDI)is a control approach that has gained popularity in flight control over the past decade.Besides the INDI law,several common additional components complement an INDI-based c...Incremental Nonlinear Dynamic Inversion(INDI)is a control approach that has gained popularity in flight control over the past decade.Besides the INDI law,several common additional components complement an INDI-based controller.This paper,the second part of a two-part series of surveys on INDI,aims to summarize the modern trends in INDI and its related components.Besides a comprehensive components specification,it addresses their most common challenges,compares different variants,and discusses proposed advances.Further important aspects of INDI are gain design,stability,and robustness.This paper also provides an overview of research conducted concerning these aspects.This paper is written in a tutorial style to familiarize researchers with the essential specifics and pitfalls of INDI and its components.At the same time,it can also serve as a reference for readers already familiar with INDI.展开更多
Ultrasound computed tomography(USCT)is a noninvasive biomedical imaging modality that offers insights into acoustic properties such as the sound speed(SS)and acoustic attenuation(AA)of the human body,enhancing diagnos...Ultrasound computed tomography(USCT)is a noninvasive biomedical imaging modality that offers insights into acoustic properties such as the sound speed(SS)and acoustic attenuation(AA)of the human body,enhancing diagnostic accuracy and therapy planning.Full waveform inversion(FWI)is a promising USCT image reconstruction method that optimizes the parameter fields of a wave propagation model via gradient-based optimization.However,twodimensional FWI methods are limited by their inability to account for three-dimensional wave propagation in the elevation direction,resulting in image artifacts.To address this problem,we propose a three-dimensional time-domain full waveform inversion algorithm to reconstruct the SS and AA distributions on the basis of a fractional Laplacian wave equation,adjoint field formulation,and gradient descent optimization.Validated by two sets of simulations,the proposed algorithm has potential for generating high-resolution and quantitative SS and AA distributions.This approach holds promise for clinical USCT applications,assisting early disease detection,precise abnormality localization,and optimized treatment planning,thus contributing to better healthcare outcomes.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
In Rayleigh wave exploration,the inversion of dispersion curves is a crucial step for obtaining subsurface stratigraphic information,characterized by its multi-parameter and multi-extremum nature.Local optimization al...In Rayleigh wave exploration,the inversion of dispersion curves is a crucial step for obtaining subsurface stratigraphic information,characterized by its multi-parameter and multi-extremum nature.Local optimization algorithms used in dispersion curve inversion are highly dependent on the initial model and are prone to being trapped in local optima,while classical global optimization algorithms often suffer from slow convergence and low solution accuracy.To address these issues,this study introduces the Osprey Optimization Algorithm(OOA),known for its strong global search and local exploitation capabilities,into the inversion of dispersion curves to enhance inversion performance.In noiseless theoretical models,the OOA demonstrates excellent inversion accuracy and stability,accurately recovering model parameters.Even in noisy models,OOA maintains robust performance,achieving high inversion precision under high-noise conditions.In multimode dispersion curve tests,OOA effectively handles higher modes due to its efficient global and local search capabilities,and the inversion results show high consistency with theoretical values.Field data from the Wyoming region in the United States and a landfill site in Italy further verify the practical applicability of the OOA.Comprehensive test results indicate that the OOA outperforms the Particle Swarm Optimization(PSO)algorithm,providing a highly accurate and reliable inversion strategy for dispersion curve inversion.展开更多
Fluid identification and anisotropic parameters characterization are crucial for shale reservoir exploration and development.However,the anisotropic reflection coefficient equation,based on the transverse isotropy wit...Fluid identification and anisotropic parameters characterization are crucial for shale reservoir exploration and development.However,the anisotropic reflection coefficient equation,based on the transverse isotropy with a vertical axis of symmetry(VTI)medium assumption,involves numerous parameters to be inverted.This complexity reduces its stability and impacts the accuracy of seismic amplitude variation with offset(AVO)inversion results.In this study,a novel anisotropic equation that includes the fluid term and Thomsen anisotropic parameters is rewritten,which reduces the equation's dimensionality and increases its stability.Additionally,the traditional Markov Chain Monte Carlo(MCMC)inversion algorithm exhibits a high rejection rate for random samples and relies on known parameter distributions such as the Gaussian distribution,limiting the algorithm's convergence and sample randomness.To address these limitations and evaluate the uncertainty of AVO inversion,the IADR-Gibbs algorithm is proposed,which incorporates the Independent Adaptive Delayed Rejection(IADR)algorithm with the Gibbs sampling algorithm.Grounded in Bayesian theory,the new algorithm introduces support points to construct a proposal distribution of non-parametric distribution and reselects the rejected samples according to the Delayed Rejection(DR)strategy.Rejected samples are then added to the support points to update the proposal distribution function adaptively.The equation rewriting method and the IADR-Gibbs algorithm improve the accuracy and robustness of AVO inversion.The effectiveness and applicability of the proposed method are validated through synthetic gather tests and practical data applications.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
Understanding the relationship between plasmon near-field enhancement and lifetime is crucial for various applications,yet their inverse correlation in single-element nanoresonators remains underexplored.Here,we exper...Understanding the relationship between plasmon near-field enhancement and lifetime is crucial for various applications,yet their inverse correlation in single-element nanoresonators remains underexplored.Here,we experimentally demonstrate an inverse correlation between these two parameters in single-element gold lateral V-shaped nanoresonators under different light polarization conditions by tuning the opening angle,through femtosecond time-resolved photoemission electron microscopy.Specifically,under p-polarized light irradiation,as the opening angle of V-shaped nanoresonators decreases,the plasmon near-field enhancement increases while the lifetime shortens simultaneously.展开更多
The 2D data processing adopted by the high-density resistivity method regards the geological structures as two degrees, which makes the results of the 2D data inversion only an approximate interpretation;the accuracy ...The 2D data processing adopted by the high-density resistivity method regards the geological structures as two degrees, which makes the results of the 2D data inversion only an approximate interpretation;the accuracy and effect can not meet the precise requirement of the inversion. Two typical models of the geological bodies were designed, and forward calculation was carried out using finite element method. The forward-modeled profiles were obtained. 1% Gaussian random error was added in the forward models and then 2D and 3D inversions using a high-density resistivity method were undertaken to realistically simulate field data and analyze the sensitivity of the 2D and 3D inversion algorithms to noise. Contrast between the 2D and 3D inversion results of least squares inversion shows that two inversion results of high-density resistivity method all can basically reflect the spatial position of an anomalous body. However, the 3D inversion can more effectively eliminate the influence of interference from Gaussian random error and better reflect the distribution of resistivity in the anomalous bodies. Overall, the 3D inversion was better than 2D inversion in terms of embodying anomalous body positions, morphology and resistivity properties.展开更多
In this paper, we revisit the core inverse introduced by Baksalary and Trenkler. We first give some new characterizations of the core inverse. Then, we give a new representation of the core inverse, which is related t...In this paper, we revisit the core inverse introduced by Baksalary and Trenkler. We first give some new characterizations of the core inverse. Then, we give a new representation of the core inverse, which is related to AT,S^（2）.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
Recently, solutions to inverse problems have been required in various engineering fields. The neural network inversion method has been studied as one of the neural network-based solutions. On the other hand, the exten...Recently, solutions to inverse problems have been required in various engineering fields. The neural network inversion method has been studied as one of the neural network-based solutions. On the other hand, the extension of the neural network to a higher-dimensional domain, e.g., complex-value or quaternion, has been proposed, and a number of higher-dimensional neural network models have been proposed. Using the quatemion, we have the advantage of expressing 3D （three-dimensional） object attitudes easily. In the quaternion domain, we can define inverse problems where the cause and the result are expressed by the quaternion. In this paper, we extend the neural network inversion method to the quatemion domain. Further, we provide the results of the computer experiments to demonstrate the process and effectiveness of our method.展开更多
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financially supported by the National Natural Science Foundation of China (Nos.22171165 and 22371170)Natural Science Foundation of Shandong Province (No.ZR2022MB080)Scientific and Technological Frontiers in Project of Henan Province(No.242102110192)。
文摘Co-assembling chiral molecules with achiral compounds via non-covalent interactions like areneperfluoroarene(AP) interactions offers an effective approach for fabricating chiral functional materials.Herein,chiral molecules L/D-PF1 and L/D-PF2 with pyrene groups were synthesized and its chiroptical properties upon co-assembly with achiral compound octafluoronaphthalene(OFN) through AP interaction were systemically studied.The co-assembly of L/D-PF1/OFN and L/D-PF2/OFN exhibited distinct chiroptical properties such as circular dichroism(CD) and circularly polarized luminescence(CPL) signals.Chirality transfer from the chirality center of L/D-PF1 and L/D-PF2 to the achiral OFN and chiral amplification were successfully achieved.Besides,no significant CPL signal was observed in the self-assembly of L/DPF1 or L/D-PF2 while co-assembly with OFN exhibited obvious CPL amplification induced by AP interaction.Notably,a reversal CD signal and CPL signal could be observed in L/D-PF2/OFN when the molar ratio changed from 1:1 to 1:2 while not found in L/D-PF1/OFN,indicating that that minor structural changes of molecules could cause large changes in assembly.In addition,a series of computational calculations were conducted to verify the AP interaction between L-PF1/L-PF2 and OFN.This work demonstrated that arene-perfluoroarene interaction could drive chiral transfer,chiral amplification and chiral inversion and provided a new method for the preparation of chiroptical materials.
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the sponsorship of National Natural Science Foundation of China(42325403)Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project of China(2024ZD1004201)。
文摘Subsurface reservoirs commonly exhibit layered structures.Conventional amplitude variation with angle(AVA)inversion,which relies on the Zoeppritz equation and its approximations,often fails to accurately estimate elastic parameters because it assumes single-interface models and ignores multiple reflections and transmission losses.To address these limitations,this study proposes a novel prestack time-frequency domain joint inversion method that utilizes the reflection matrix method(RMM)as the forward operator.The RMM accurately simulates wave propagation in layered media,while the joint inversion framework minimizes the misfit between observed and synthetic data in both the time and frequency domains.By incorporating Bayesian theory to optimize the inversion process,the method effectively balances contributions from both time-domain waveforms and frequency-domain spectral information through a weighting factor.Tests on both synthetic data and field data demonstrate that the proposed method outperforms conventional AVA inversion and time-domain waveform inversion in accuracy and robustness.Furthermore,the method demonstrates good robustness against variations in initial models,random noise,and coherent noise interference.This study provides a practical and effective approach for high-precision reservoir characterization,with potential applications in complex layered media.
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 Fundamental Research Funds for the Central Universities,CHD300102264715National Key Research and Development Program of China under Grant 2021YFA0716902Natural Science Basic Research Program of Shaanxi 2024JCYBMS-199。
文摘Young's modulus and Poisson's ratio are crucial parameters for reservoir characterization and rock brittleness evaluation.Conventional methods often rely on indirect computation or approximations of the Zoeppritz equations to estimate Young's modulus,which can introduce cumulative errors and reduce the accuracy of inversion results.To address these issues,this paper introduces the analytical solution of the Zoeppritz equation into the inversion process.The equation is re-derived and expressed in terms of Young's modulus,Poisson's ratio,and density.Within the Bayesian framework,we construct an objective function for the joint inversion of PP and PS waves.Traditional gradient-based algorithms often suffer from low precision and the computational complexity.In this study,we address limitations of conventional approaches related to low precision and complicated code by using Circle chaotic mapping,Levy flights,and Gaussian mutation to optimize the quantum particle swarm optimization(QPSO),named improved quantum particle swarm optimization(IQPSO).The IQPSO demonstrates superior global optimization capabilities.We test the proposed inversion method with both synthetic and field data.The test results demonstrate the proposed method's feasibility and effectiveness,indicating an improvement in inversion accuracy over traditional methods.
文摘Incremental Nonlinear Dynamic Inversion(INDI)is a control approach that has gained popularity in flight control over the past decade.Besides the INDI law,several common additional components complement an INDI-based controller.This paper,the second part of a two-part series of surveys on INDI,aims to summarize the modern trends in INDI and its related components.Besides a comprehensive components specification,it addresses their most common challenges,compares different variants,and discusses proposed advances.Further important aspects of INDI are gain design,stability,and robustness.This paper also provides an overview of research conducted concerning these aspects.This paper is written in a tutorial style to familiarize researchers with the essential specifics and pitfalls of INDI and its components.At the same time,it can also serve as a reference for readers already familiar with INDI.
基金supported by the National Key Research and Development Program of China(2022YFA1404400)the National Natural Science Foundation of China(62122072,12174368,61705216,62405306)+4 种基金Anhui Provincial Department of Science and Technology(202203a07020020,18030801138)Anhui Provincial Natural Science Foundation(2308085QA21,2408085QF187)the USTC Research Funds of the Double First-Class Initiative(YD2090002015)the Institute of Artificial Intelligence at Hefei Comprehensive National Science Center(23YGXT005)the Fundamental Research Funds for the Central Universities(WK2090000083).
文摘Ultrasound computed tomography(USCT)is a noninvasive biomedical imaging modality that offers insights into acoustic properties such as the sound speed(SS)and acoustic attenuation(AA)of the human body,enhancing diagnostic accuracy and therapy planning.Full waveform inversion(FWI)is a promising USCT image reconstruction method that optimizes the parameter fields of a wave propagation model via gradient-based optimization.However,twodimensional FWI methods are limited by their inability to account for three-dimensional wave propagation in the elevation direction,resulting in image artifacts.To address this problem,we propose a three-dimensional time-domain full waveform inversion algorithm to reconstruct the SS and AA distributions on the basis of a fractional Laplacian wave equation,adjoint field formulation,and gradient descent optimization.Validated by two sets of simulations,the proposed algorithm has potential for generating high-resolution and quantitative SS and AA distributions.This approach holds promise for clinical USCT applications,assisting early disease detection,precise abnormality localization,and optimized treatment planning,thus contributing to better healthcare outcomes.
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sponsored by China Geological Survey Project(DD20243193 and DD20230206508).
文摘In Rayleigh wave exploration,the inversion of dispersion curves is a crucial step for obtaining subsurface stratigraphic information,characterized by its multi-parameter and multi-extremum nature.Local optimization algorithms used in dispersion curve inversion are highly dependent on the initial model and are prone to being trapped in local optima,while classical global optimization algorithms often suffer from slow convergence and low solution accuracy.To address these issues,this study introduces the Osprey Optimization Algorithm(OOA),known for its strong global search and local exploitation capabilities,into the inversion of dispersion curves to enhance inversion performance.In noiseless theoretical models,the OOA demonstrates excellent inversion accuracy and stability,accurately recovering model parameters.Even in noisy models,OOA maintains robust performance,achieving high inversion precision under high-noise conditions.In multimode dispersion curve tests,OOA effectively handles higher modes due to its efficient global and local search capabilities,and the inversion results show high consistency with theoretical values.Field data from the Wyoming region in the United States and a landfill site in Italy further verify the practical applicability of the OOA.Comprehensive test results indicate that the OOA outperforms the Particle Swarm Optimization(PSO)algorithm,providing a highly accurate and reliable inversion strategy for dispersion curve inversion.
基金the sponsorship of the Key Technology for Geophysical Prediction of Ultra-Deep Carbonate Reservoirs(P24240)the National Natural Science Foundation of China(U24B2020)the National Science and Technology Major Project of China for New Oil and Gas Exploration and Development(Grant No.2024ZD1400102)。
文摘Fluid identification and anisotropic parameters characterization are crucial for shale reservoir exploration and development.However,the anisotropic reflection coefficient equation,based on the transverse isotropy with a vertical axis of symmetry(VTI)medium assumption,involves numerous parameters to be inverted.This complexity reduces its stability and impacts the accuracy of seismic amplitude variation with offset(AVO)inversion results.In this study,a novel anisotropic equation that includes the fluid term and Thomsen anisotropic parameters is rewritten,which reduces the equation's dimensionality and increases its stability.Additionally,the traditional Markov Chain Monte Carlo(MCMC)inversion algorithm exhibits a high rejection rate for random samples and relies on known parameter distributions such as the Gaussian distribution,limiting the algorithm's convergence and sample randomness.To address these limitations and evaluate the uncertainty of AVO inversion,the IADR-Gibbs algorithm is proposed,which incorporates the Independent Adaptive Delayed Rejection(IADR)algorithm with the Gibbs sampling algorithm.Grounded in Bayesian theory,the new algorithm introduces support points to construct a proposal distribution of non-parametric distribution and reselects the rejected samples according to the Delayed Rejection(DR)strategy.Rejected samples are then added to the support points to update the proposal distribution function adaptively.The equation rewriting method and the IADR-Gibbs algorithm improve the accuracy and robustness of AVO inversion.The effectiveness and applicability of the proposed method are validated through synthetic gather tests and practical data applications.
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National Key Research and Development Program of China(2022YFA1604304,2022YFA1604303)National Natural Science Foundation of China(12404356,12374341)+2 种基金Department of Science and Technology of Jilin Province(YDZJ202401607ZYTS)Natural Science Foundation of Chongqing Municipality(CSTB2023NSCQMSX1026,CSTB2023NSCQ-MSX0302,CSTB2023NSCQMSX0708)111 Project(D17017)。
文摘Understanding the relationship between plasmon near-field enhancement and lifetime is crucial for various applications,yet their inverse correlation in single-element nanoresonators remains underexplored.Here,we experimentally demonstrate an inverse correlation between these two parameters in single-element gold lateral V-shaped nanoresonators under different light polarization conditions by tuning the opening angle,through femtosecond time-resolved photoemission electron microscopy.Specifically,under p-polarized light irradiation,as the opening angle of V-shaped nanoresonators decreases,the plasmon near-field enhancement increases while the lifetime shortens simultaneously.
基金Projects(41074085,41374118)supported by the National Natural Science Foundation of ChinaProject(20120162110015)supported by Doctoral Fund of Ministry of Education of ChinaProject(NCET-12-0551)supported by Program for New Century Excellent Talents in University,China
文摘The 2D data processing adopted by the high-density resistivity method regards the geological structures as two degrees, which makes the results of the 2D data inversion only an approximate interpretation;the accuracy and effect can not meet the precise requirement of the inversion. Two typical models of the geological bodies were designed, and forward calculation was carried out using finite element method. The forward-modeled profiles were obtained. 1% Gaussian random error was added in the forward models and then 2D and 3D inversions using a high-density resistivity method were undertaken to realistically simulate field data and analyze the sensitivity of the 2D and 3D inversion algorithms to noise. Contrast between the 2D and 3D inversion results of least squares inversion shows that two inversion results of high-density resistivity method all can basically reflect the spatial position of an anomalous body. However, the 3D inversion can more effectively eliminate the influence of interference from Gaussian random error and better reflect the distribution of resistivity in the anomalous bodies. Overall, the 3D inversion was better than 2D inversion in terms of embodying anomalous body positions, morphology and resistivity properties.
基金Supported by the National Natural Science Foundation of China(11271105)the Key Research Project of Educational Department of Hubei Province(D20122202)Youth Research Project of Educational Department of Hubei Province(B20122203)
文摘In this paper, we revisit the core inverse introduced by Baksalary and Trenkler. We first give some new characterizations of the core inverse. Then, we give a new representation of the core inverse, which is related to AT,S^（2）.
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.
文摘Recently, solutions to inverse problems have been required in various engineering fields. The neural network inversion method has been studied as one of the neural network-based solutions. On the other hand, the extension of the neural network to a higher-dimensional domain, e.g., complex-value or quaternion, has been proposed, and a number of higher-dimensional neural network models have been proposed. Using the quatemion, we have the advantage of expressing 3D （three-dimensional） object attitudes easily. In the quaternion domain, we can define inverse problems where the cause and the result are expressed by the quaternion. In this paper, we extend the neural network inversion method to the quatemion domain. Further, we provide the results of the computer experiments to demonstrate the process and effectiveness of our method.
