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
This work deals with an inverse scattering problem for the Schrodinger operator on a star-shaped graph with one semi-infinite branch.Using the high-frequency asymptotic behaviour of the reflection coefficient,first we...This work deals with an inverse scattering problem for the Schrodinger operator on a star-shaped graph with one semi-infinite branch.Using the high-frequency asymptotic behaviour of the reflection coefficient,first we provide the identifiability of the geometry of this star-shaped graph:the number of edges and their lengths.Under some assumptions on the geometry of the graph,the main result states that the measurement of one reflection coefficient,together with the scattering data corresponding to the infinite branch,associated with Robin boundary conditions at the external nodes of the graph,can uniquely determine the parameters of the boundary conditions and the potentials on the whole interval which is already known in a half-interval.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
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
Efficient solar light harvesting is essential for high-performance photocatalysts.Here,Rigorous CoupledWave Analysis(RCWA)computational method is used to investigate and optimize the optical absorption of TiO_(2)-BiVO...Efficient solar light harvesting is essential for high-performance photocatalysts.Here,Rigorous CoupledWave Analysis(RCWA)computational method is used to investigate and optimize the optical absorption of TiO_(2)-BiVO_(4) inverse opal(IO)structures under varying light incidence angles and pore-filling medium(air or water).Simulations were validated against experimental reflectance data.They revealed that small-pore IOs strongly absorb in the UV-C and UV-B regions due to the slow photon effect,making them ideal for sterilization and water disinfection.Medium-and large-pore IOs benefit from additional slow photon effect at the 2nd order photonic band gap,enhancing absorption across both UV and visible regions.Medium-pore IOs are suited for indoor air treatment and water purification,while large-pore IOs with the highest photon flux enhancement enable solar-driven photocatalysis such as outdoor pollutant removal and hydrogen production.For all tested IO designs,the absorbed photon flux exceeds that of equivalent planar slabs,highlighting the advantage of photonic structuring for sustainable photocatalytic applications.展开更多
The quasi-monochromatic,continuously energy-tunable,and high-brightness gamma rays that are produced by an inverse Compton scattering(ICS)light source provide an ideal probe for gamma-ray imaging.However,owing to the ...The quasi-monochromatic,continuously energy-tunable,and high-brightness gamma rays that are produced by an inverse Compton scattering(ICS)light source provide an ideal probe for gamma-ray imaging.However,owing to the influence of the intrinsic energy-angle correlation spectrum of this type of light source,monochromatic computed tomography(CT),especially in the gamma-ray energy region,can only be realized in a low-efficiency manner,similar to first-generation CT.A dual-energy scan scheme with a large imaging field of view(FOV)was developed in this study to improve the imaging efficiency.The effectiveness of this scheme was demonstrated based on the beam parameters of a typical ICS light source using Monte Carlo simulations.By leveraging the principle of basis material decomposition,the influence of the energyangle correlation spectrum on CT reconstruction was corrected,and a monochromatic CT image of the imaging object was accurately reconstructed.Furthermore,the electron density and effective atomic number of the imaging object could be obtained simultaneously.展开更多
We discuss recent progress in using machine-learning(ML)-enabled inverse design techniques applied to photonic devices and components.Specifically,we highlight the design of optical sources,including fiber and semicon...We discuss recent progress in using machine-learning(ML)-enabled inverse design techniques applied to photonic devices and components.Specifically,we highlight the design of optical sources,including fiber and semiconductor lasers,as well as Raman and semiconductor optical amplifiers.Although inverse design approaches for optical detectors remain relatively underexplored,we examine optical layers,particularly metamaterial absorbers,as promising candidates for high-performance optical detection.In addition,we underscore advancements in inverse designing passive optical components,including beam splitters,gratings,and optical fibers.These optical blocks are fundamental in developing next-generation standalone optical communication systems and optical sensing networks,including integrated sensing and communication technologies.While categorizing various reported deep learning architectures across five paradigms,we offer a paradigm-based perspective that reveals how different ML techniques function within modern inverse design methods and enable fast,data-driven solutions that significantly reduce design time and computational demands compared with traditional optimization methods.展开更多
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.
文摘This work deals with an inverse scattering problem for the Schrodinger operator on a star-shaped graph with one semi-infinite branch.Using the high-frequency asymptotic behaviour of the reflection coefficient,first we provide the identifiability of the geometry of this star-shaped graph:the number of edges and their lengths.Under some assumptions on the geometry of the graph,the main result states that the measurement of one reflection coefficient,together with the scattering data corresponding to the infinite branch,associated with Robin boundary conditions at the external nodes of the graph,can uniquely determine the parameters of the boundary conditions and the potentials on the whole interval which is already known in a half-interval.
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 FNRS-FRFC,the Walloon Region,and the University of Namur(Conventions No.2.5020.11,GEQ U.G006.15,1610468,RW/GEQ2016 et U.G011.22)funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska Curie grant agreement n°101034383。
文摘Efficient solar light harvesting is essential for high-performance photocatalysts.Here,Rigorous CoupledWave Analysis(RCWA)computational method is used to investigate and optimize the optical absorption of TiO_(2)-BiVO_(4) inverse opal(IO)structures under varying light incidence angles and pore-filling medium(air or water).Simulations were validated against experimental reflectance data.They revealed that small-pore IOs strongly absorb in the UV-C and UV-B regions due to the slow photon effect,making them ideal for sterilization and water disinfection.Medium-and large-pore IOs benefit from additional slow photon effect at the 2nd order photonic band gap,enhancing absorption across both UV and visible regions.Medium-pore IOs are suited for indoor air treatment and water purification,while large-pore IOs with the highest photon flux enhancement enable solar-driven photocatalysis such as outdoor pollutant removal and hydrogen production.For all tested IO designs,the absorbed photon flux exceeds that of equivalent planar slabs,highlighting the advantage of photonic structuring for sustainable photocatalytic applications.
基金supported by the National Natural Science Foundation of China(Nos.12375157,12027902,and 11905011)。
文摘The quasi-monochromatic,continuously energy-tunable,and high-brightness gamma rays that are produced by an inverse Compton scattering(ICS)light source provide an ideal probe for gamma-ray imaging.However,owing to the influence of the intrinsic energy-angle correlation spectrum of this type of light source,monochromatic computed tomography(CT),especially in the gamma-ray energy region,can only be realized in a low-efficiency manner,similar to first-generation CT.A dual-energy scan scheme with a large imaging field of view(FOV)was developed in this study to improve the imaging efficiency.The effectiveness of this scheme was demonstrated based on the beam parameters of a typical ICS light source using Monte Carlo simulations.By leveraging the principle of basis material decomposition,the influence of the energyangle correlation spectrum on CT reconstruction was corrected,and a monochromatic CT image of the imaging object was accurately reconstructed.Furthermore,the electron density and effective atomic number of the imaging object could be obtained simultaneously.
基金the School of Engineering and Built Environment at Anglia Ruskin University,UK,for the supportthe support of IRC-CSS and the Electrical Engineering Department,KFUPM,Saudi Arabia。
文摘We discuss recent progress in using machine-learning(ML)-enabled inverse design techniques applied to photonic devices and components.Specifically,we highlight the design of optical sources,including fiber and semiconductor lasers,as well as Raman and semiconductor optical amplifiers.Although inverse design approaches for optical detectors remain relatively underexplored,we examine optical layers,particularly metamaterial absorbers,as promising candidates for high-performance optical detection.In addition,we underscore advancements in inverse designing passive optical components,including beam splitters,gratings,and optical fibers.These optical blocks are fundamental in developing next-generation standalone optical communication systems and optical sensing networks,including integrated sensing and communication technologies.While categorizing various reported deep learning architectures across five paradigms,we offer a paradigm-based perspective that reveals how different ML techniques function within modern inverse design methods and enable fast,data-driven solutions that significantly reduce design time and computational demands compared with traditional optimization methods.
