Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce different...Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce differential equations,constitutive relations,and boundary conditions within the loss function provides a physically grounded alternative to traditional data-driven models,particularly for solid and structural mechanics,where data are often limited or noisy.This review offers a comprehensive assessment of recent developments in PINNs,combining bibliometric analysis,theoretical foundations,application-oriented insights,and methodological innovations.A biblio-metric survey indicates a rapid increase in publications on PINNs since 2018,with prominent research clusters focused on numerical methods,structural analysis,and forecasting.Building upon this trend,the review consolidates advance-ments across five principal application domains,including forward structural analysis,inverse modeling and parameter identification,structural and topology optimization,assessment of structural integrity,and manufacturing processes.These applications are propelled by substantial methodological advancements,encompassing rigorous enforcement of boundary conditions,modified loss functions,adaptive training,domain decomposition strategies,multi-fidelity and transfer learning approaches,as well as hybrid finite element–PINN integration.These advances address recurring challenges in solid mechanics,such as high-order governing equations,material heterogeneity,complex geometries,localized phenomena,and limited experimental data.Despite remaining challenges in computational cost,scalability,and experimental validation,PINNs are increasingly evolving into specialized,physics-aware tools for practical solid and structural mechanics applications.展开更多
This study focuses on a new and high-efficiency approach in a unified sense of accurately simulating strength-degrading effects for geomaterials,including non-symmetric hardening-to-softening effects in tension and co...This study focuses on a new and high-efficiency approach in a unified sense of accurately simulating strength-degrading effects for geomaterials,including non-symmetric hardening-to-softening effects in tension and compression as well as non-symmetric tensile and compressive stiffness-degrading effects during unloading.It is intended to bypass both modeling and numerical complexities involved in existing approaches.To this goal,new elastoplastic equations are established with new numerical techniques.With a decoupling technique of treating tension-compression asymmetry,the foregoing complex effects are automatically incorporated as inherent response features of the new elastoplastic equations,thus bypassing usual modeling complexities.A new numerical technique of renormalizing piecewise spline functions is introduced to resolve the central yet tough issue of obtaining accurate and unified expressions for the tensile and compressive strength functions,thus bypassing usual numerical complexities and uncertainties in treating numerous unknown parameters and multiple ad hoc criteria.As such,the new approach is not only of wide applicability for various geomaterials but also of high computational efficiency with no more than three adjustable parameters.Toward validating the efficacy of the new approach,numerical examples for granite,salt rock,and sandstone-concrete combined body as well as plain concrete,high-performance concrete,and ultrahigh-performance concrete are presented by comparing model predictions with multiple data sets for strength-degrading effects in tension and compression.展开更多
This paper presents an isogeometric boundary element method(IGABEM)for transient heat conduction analysis.The Non-Uniform Rational B-spline(NURBS)basis functions,which are used to construct the geometry of the structu...This paper presents an isogeometric boundary element method(IGABEM)for transient heat conduction analysis.The Non-Uniform Rational B-spline(NURBS)basis functions,which are used to construct the geometry of the structures,are employed to discretize the physical unknowns in the boundary integral formulations of the governing equations.Bezier extraction technique is employed to accelerate the evaluation of NURBS basis functions.We adopt a radial integration method to address the additional domain integrals.The numerical examples demonstrate the advantage of IGABEM in dimension reduction and the seamless connection between CAD and numerical analysis.展开更多
We present two approaches to system identification, i.e. the identification of partial differentialequations (PDEs) from measurement data. The first is a regression-based variational systemidentification procedure tha...We present two approaches to system identification, i.e. the identification of partial differentialequations (PDEs) from measurement data. The first is a regression-based variational systemidentification procedure that is advantageous in not requiring repeated forward model solves andhas good scalability to large number of differential operators. However it has strict data typerequirements needing the ability to directly represent the operators through the available data.The second is a Bayesian inference framework highly valuable for providing uncertaintyquantification, and flexible for accommodating sparse and noisy data that may also be indirectquantities of interest. However, it also requires repeated forward solutions of the PDE modelswhich is expensive and hinders scalability. We provide illustrations of results on a model problemfor pattern formation dynamics, and discuss merits of the presented methods.展开更多
We present a framework that couples a high-fidelity compositional reservoir simulator with Bayesian optimization(BO)for injection well scheduling optimization in geological carbon sequestration.This work represents on...We present a framework that couples a high-fidelity compositional reservoir simulator with Bayesian optimization(BO)for injection well scheduling optimization in geological carbon sequestration.This work represents one of the first at tempts to apply BO and high-fidelity physics models to geological carbon storage.The implicit parallel accurate reservoir simulator(IPARS)is utilized to accurately capture the underlying physical processes during CO_(2)sequestration.IPARS provides a framework for several flow and mechanics models and thus supports both stand-alone and coupled simulations.In this work,we use the compositional flow module to simulate the geological carbon storage process.The compositional flow model,which includes a hysteretic three-phase relative permeability model,accounts for three major CO_(2)trapping mechanisms:structural trapping,residual gas trapping,and solubility trapping.Furthermore,IPARS is coupled to the International Business Machines(IBM)Corporation Bayesian Optimization Accelerator(BOA)for parallel optimizations of CO_(2)injection strategies during field-scale CO_(2)sequestration.BO builds a probabilistic surrogate for the objective function using a Bayesian machine learning algorithm-the Gaussian process regression,and then uses an acquisition function that leverages the uncertainty in the surrogate to decide where to sample.The IBM BOA addresses the three weaknesses of standard BO that limits its scalability in that IBM BOA supports parallel(batch)executions,scales better for high-dimensional problems,and is more robust to initializations.We demonstrate these merits by applying the algorithm in the optimization of the CO_(2)injection schedule in the Cranfield site in Mississippi,USA,using field data.The optimized injection schedule achieves 16%more gas storage volume and 56%less water/surfactant usage compared with the baseline.The performance of BO is compared with that of a genetic algorithm(GA)and a covariance matrix adaptation(CMA)-evolution strategy(ES).The results demonstrate the superior performance of BO,in that it achieves a competitive objective function value with over 60%fewer forward model evaluations.展开更多
To inhibit the agglomeration of tin-based nanomaterials and simplify the complicated synthesis process,a facile and eco-friendly self-formed template method is reported to synthesize tin submicron spheres dispersed in...To inhibit the agglomeration of tin-based nanomaterials and simplify the complicated synthesis process,a facile and eco-friendly self-formed template method is reported to synthesize tin submicron spheres dispersed in nitrogen-doped porous carbon(Sn/NPC)by pyrolysis of a mixture of disodium stannous citrate and urea.The vital point of this strategy is the formation of Na_(2)CO_(3)templates during pyrolysis.This self-formed Na_(2)CO_(3)acts as porous templates to support the formation of NPC.The obtained NPC provides good electronic conductivity,ample defects,and more active sites.Serving as anode for Li-ion batteries,the Sn/NPC electrode obtains a stable discharge capacity of 674.1 mAh/g after 150 cycles at 0.1 A/g.Especially,a high discharge capacity of 331.2 mAh/g can be achieved after 1100 cycles at 3 A/g.Additionally,a full cell coupled with LiCoO_(2)as cathode yields a discharge capacity of 524.8 mAh/g after 150 cycles at 0.1 A/g.In-situ XRD is implemented to investigate the alloying/dealloying reaction mechanisms.Density functional theory calculation ulteriorly explicates that NPC heightens intrinsic electronic conductivity,and NPC especially pyrrolic-N and pyridinic-N doping facilitates the Li-adsorption ability.Climbing image nudged elastic band method reveals low Li~+diffusion energy barrier in presence of N atoms,which accounts for the terrific electrochemical properties of Sn/NPC electrode.展开更多
Graph learning,when used as a semi-supervised learning(SSL)method,performs well for classification tasks with a low label rate.We provide a graph-based batch active learning pipeline for pixel/patch neighborhood multi...Graph learning,when used as a semi-supervised learning(SSL)method,performs well for classification tasks with a low label rate.We provide a graph-based batch active learning pipeline for pixel/patch neighborhood multi-or hyperspectral image segmentation.Our batch active learning approach selects a collection of unlabeled pixels that satisfy a graph local maximum constraint for the active learning acquisition function that determines the relative importance of each pixel to the classification.This work builds on recent advances in the design of novel active learning acquisition functions(e.g.,the Model Change approach in arXiv:2110.07739)while adding important further developments including patch-neighborhood image analysis and batch active learning methods to further increase the accuracy and greatly increase the computational efficiency of these methods.In addition to improvements in the accuracy,our approach can greatly reduce the number of labeled pixels needed to achieve the same level of the accuracy based on randomly selected labeled pixels.展开更多
The Burgers＇ equation with uncertain initial and boundary conditions is approximated using a Polynomial Chaos Expansion （PCE） approach where the solution is represented as a series of stochastic, orthogonal polynom...The Burgers＇ equation with uncertain initial and boundary conditions is approximated using a Polynomial Chaos Expansion （PCE） approach where the solution is represented as a series of stochastic, orthogonal polynomials. The resulting truncated PCE system is solved using a novel numerical discretization method based on spatial derivative operators satisfying the summation by parts property and weak boundary conditions to ensure stability. The resulting PCE solution yields an accurate quantitative description of the stochastic evolution of the system, provided that appropriate boundary conditions are available. The specification of the boundary data is shown to influence the solution; we will discuss the problematic implications of the lack of precisely characterized boundary data and possible ways of imposing stable and accurate boundary conditions.展开更多
We investigate primal and mixed u−p isogeometric collocation methods for application to nearly-incompressible isotropic elasticity.The primal method employs Navier’s equations in terms of the displacement unknowns,an...We investigate primal and mixed u−p isogeometric collocation methods for application to nearly-incompressible isotropic elasticity.The primal method employs Navier’s equations in terms of the displacement unknowns,and the mixed method employs both displacement and pressure unknowns.As benchmarks for what might be considered acceptable accuracy,we employ constant-pressure Abaqus finite elements that are widely used in engineering applications.As a basis of comparisons,we present results for compressible elasticity.All the methods were completely satisfactory for the compressible case.However,results for low-degree primal methods exhibited displacement locking and in general deteriorated in the nearly-incompressible case.The results for the mixed methods behaved very well for two of the problems we studied,achieving levels of accuracy very similar to those for the compressible case.The third problem,which we consider a“torture test”presented a more complex story for the mixed methods in the nearly-incompressible case.展开更多
New and perhaps unexpected progress in rate-independent elastoplastic modeling is reported with a unified approach toward simulating widely ranging non-elastic effects of various advanced engineering materials such as...New and perhaps unexpected progress in rate-independent elastoplastic modeling is reported with a unified approach toward simulating widely ranging non-elastic effects of various advanced engineering materials such as metals,shape memory alloys,granular materials,fiber-reinforced composites,as well as crystalline solids,etc.This progress originates from a simple idea of bypassing inherent limitations of usual elastoplastic formulations centered on the notion of yielding.With no reference to any yield criteria,the plastic strain-rate should be induced at all stress levels in a more realistic sense that it is small for stresses within a classical yield surface and becomes appreciable for stresses close to and on this surface.A new and unified flow rule for the plastic strain-rate is then proposed of the same smooth form for all cases of both the stress level and the stress rate.Without imposing the ad hoc simplified conditions introduced in usual Prandtl-Reuss equations,new elastoplastic equations are then established by incorporating such small deviations from realistic behaviors as neglected just by postulating these conditions.It turns out that the new equations are not only essentially simpler in both conceptual and structural formulations,but can automatically as inherent response features incorporate significant effects excluded from usual Prandtl-Reuss equations,such as the yielding and unloading behaviors with smooth transitions,the pseudo-elastic effect with hysteresis loops,the non-elastic recovery during unloading as well as failure effects under either monotone or cyclic loading conditions,etc.Since such effects not only go beyond the scope of usual elastoplastic equations but can be only partially simulated even if augmented constitutive equations are postulated toward further characterizing damaging and fracturing effects resulting from evolving micro-defects and macro-cracks,it may be probably surprising that now the new equations of essentially simpler structure not only can in a unified manner simulate all these effects but also can bypass numerical complexities in integrating various rate constitutive equations of complex structures.New results in treating long-standing issues in a few respects are presented,including(i)the yielding and the unloading behaviors with smooth transitions,(ii)the non-elastic recovery during unloading,(iii)the pseudo-elastic effect as extraordinary Bauschinger effect,(iv)failure effects under monotone and cyclic loading,(v)anisotropic multi-mode failure effects of unidirectional composites,(vi)new formulation of crystal elastoplasticity without involving non-uniqueness and singularity issues,(vii)non-normality effects for non-proportional multi-axial loading cases,and(viii)high efficiency algorithms for simulating multi-axial fatigue effects.展开更多
Topology optimization(TO)has become a core computational paradigm for structural design by defining optimality through physics-based objectives and constraints.However,practical engineering design often involves incom...Topology optimization(TO)has become a core computational paradigm for structural design by defining optimality through physics-based objectives and constraints.However,practical engineering design often involves incomplete and imperfect physical modeling due to multi-physics coupling,manufacturing uncertainty,and computational constraints,leaving critical design factors insufficiently captured in purely physics-driven formulations.In parallel,data-driven and generative methods have enabled rapid topology generation and intent-aware design exploration,yet often weaken explicit optimality guarantees.This review argues that these seemingly divergent developments can be organized under a unified information-physics perspective.We term this emerging field Topology Optimization Informatics(TOI):optimal structural design is obtained through the joint modeling and optimization of physical laws and design-relevant information.We first summarize the integration of artificial intelligence(AI)and TO into two major paradigms:AI-based one-shot TO,which learns mappings or distributions of near-optimal designs from data and prioritizes fast generation and diversity,and AI-enhanced iterative TO,which embeds learning-based modules into the classical solver-in-the-loop pipeline while keeping the underlying governing equations unchanged.Finally,we show that traditionally separate tasks—design control,computational acceleration,and fidelity enhancement—can be interpreted as different manifestations of information-physics co-modeling within a single optimization framework,thereby clarifying their connections and design implications and outlining opportunities for semantic-and data-enabled next-generation structural design.展开更多
Potential damage in composite structures caused by hail ice impact is an essential safety threat to the aircraft in flight.In this study,a nonlinear finite element(FE)model is developed to investigate the dynamic resp...Potential damage in composite structures caused by hail ice impact is an essential safety threat to the aircraft in flight.In this study,a nonlinear finite element(FE)model is developed to investigate the dynamic response and damage behavior of hybrid corrugated sandwich structures subjected to high velocity hail ice impact.The impact and breaking behavior of hail are described using the FE-smoothed particle hydrodynamics(FE-SPH)method.A rate-dependent progressive damage model is employed to capture the intra-laminar damage response;cohesive element and surface-based cohesive contact are implemented to predict the inter-laminar delamination and sheet/core debonding phenomena respectively.The transient processes of sandwich structure under different hail ice impact conditions are analyzed.Comparative analysis is conducted to address the influences of core shape and impact position on the impact performance of sandwich structures and the corresponding energy absorption characteristics are also revealed.展开更多
The paper applied the isogeometric boundary element method(IGABEM)to thermoelastic problems.The Non-Uniform Rational B-splines(NURBS)used to construct geometric models are employed to discretize the boundary integral ...The paper applied the isogeometric boundary element method(IGABEM)to thermoelastic problems.The Non-Uniform Rational B-splines(NURBS)used to construct geometric models are employed to discretize the boundary integral formulation of the governing equation.Due to the existence of thermal stress,the domain integral term appears in the boundary integral equation.We resolve this problem by incorporating radial integration method into IGABEM which converts the domain integral to the boundary integral.In this way,IGABEM can maintain its advantages in dimensionality reduction and more importantly,seamless integration of CAD and numerical analysis based on boundary representation.The algorithm is verified by numerical examples.展开更多
This paper proposes a novel optimization framework in passive control techniques to reduce noise pollution.The geometries of the structures are represented by Catmull-Clark subdivision surfaces,which are able to build...This paper proposes a novel optimization framework in passive control techniques to reduce noise pollution.The geometries of the structures are represented by Catmull-Clark subdivision surfaces,which are able to build gap-free Computer-Aided Design models and meanwhile tackle the extraordinary points that are commonly encountered in geometricmodelling.The acoustic fields are simulated using the isogeometric boundary elementmethod,and a density-based topology optimization is conducted to optimize distribution of sound-absorbing materials adhered to structural surfaces.The approach enables one to perform acoustic optimization from Computer-Aided Design models directly without needingmeshing and volume parameterization,thereby avoiding the geometric errors and time-consuming preprocessing steps in conventional simulation and optimization methods.The effectiveness of the present method is demonstrated by three dimensional numerical examples.展开更多
During April 20-22,2022,colleagues and friends gathered at the Institute of Pure&Applied Mathematics(IPAM),at the University of California at Los Angeles to celebrate Professor Stanley Osher's 8Oth birthday in...During April 20-22,2022,colleagues and friends gathered at the Institute of Pure&Applied Mathematics(IPAM),at the University of California at Los Angeles to celebrate Professor Stanley Osher's 8Oth birthday in a conference focusing on recent developments in"Optimization,Shape analysis,High-dimensional differential equations in science and Engineering,and machine learning Research(OSHER)"This conference hosted in-person talks by mathematicians,scientists,and industrial professionals worldwide.Those who could not attend extended their warm regards and expressed their appreciation for Professor Osher.展开更多
Isogeometric analysis (IGA) is known to showadvanced features compared to traditional finite element approaches.Using IGA one may accurately obtain the geometrically nonlinear bending behavior of plates with functiona...Isogeometric analysis (IGA) is known to showadvanced features compared to traditional finite element approaches.Using IGA one may accurately obtain the geometrically nonlinear bending behavior of plates with functionalgrading (FG). However, the procedure is usually complex and often is time-consuming. We thus put forward adeep learning method to model the geometrically nonlinear bending behavior of FG plates, bypassing the complexIGA simulation process. A long bidirectional short-term memory (BLSTM) recurrent neural network is trainedusing the load and gradient index as inputs and the displacement responses as outputs. The nonlinear relationshipbetween the outputs and the inputs is constructed usingmachine learning so that the displacements can be directlyestimated by the deep learning network. To provide enough training data, we use S-FSDT Von-Karman IGA andobtain the displacement responses for different loads and gradient indexes. Results show that the recognition erroris low, and demonstrate the feasibility of deep learning technique as a fast and accurate alternative to IGA formodeling the geometrically nonlinear bending behavior of FG plates.展开更多
Anti-plane deformation of square lattices containing interphases is analyzed. It is assumed that lattices are linear elastic but not necessarily isotropic, whereas interphases exhibit non-linear elastic behavior. It i...Anti-plane deformation of square lattices containing interphases is analyzed. It is assumed that lattices are linear elastic but not necessarily isotropic, whereas interphases exhibit non-linear elastic behavior. It is demonstrated that such problems can be treated effectively using Green＇s functions, which allow to eliminate the degrees of freedom outside of the interphase. Illustrative numerical examples focus on the determination of applied stresses leading to lattice instability.展开更多
We propose a new framework for the sampling,compression,and analysis of distributions of point sets and other geometric objects embedded in Euclidean spaces.Our approach involves constructing a tensor called the RaySe...We propose a new framework for the sampling,compression,and analysis of distributions of point sets and other geometric objects embedded in Euclidean spaces.Our approach involves constructing a tensor called the RaySense sketch,which captures nearest neighbors from the underlying geometry of points along a set of rays.We explore various operations that can be performed on the RaySense sketch,leading to different properties and potential applications.Statistical information about the data set can be extracted from the sketch,independent of the ray set.Line integrals on point sets can be efficiently computed using the sketch.We also present several examples illustrating applications of the proposed strategy in practical scenarios.展开更多
Light-induced ultrafast spin dynamics in materials is of great importance for developments of spintronics and magnetic storage technology.Recent progresses include ultrafast demagnetization,magnetic switching,and magn...Light-induced ultrafast spin dynamics in materials is of great importance for developments of spintronics and magnetic storage technology.Recent progresses include ultrafast demagnetization,magnetic switching,and magnetic phase transitions,while the ultrafast generation of magnetism is hardly achieved.Here,a strong lightinduced magnetization(up to 0.86μBper formula unit)is identified in non-magnetic monolayer molybdenum disulfide(MoS_(2)).With the state-of-the-art time-dependent density functional theory simulations,we demonstrate that the out-of-plane magnetization can be induced by circularly polarized laser,where chiral phonons play a vital role.The phonons strongly modulate spin-orbital interactions and promote electronic transitions between the two conduction band states,achieving an effective magnetic field~380 T.Our study provides important insights into the ultrafast magnetization and spin-phonon coupling dynamics,facilitating effective light-controlled valleytronics and magnetism.展开更多
基金funded by National Research Council of Thailand(contract No.N42A671047).
文摘Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce differential equations,constitutive relations,and boundary conditions within the loss function provides a physically grounded alternative to traditional data-driven models,particularly for solid and structural mechanics,where data are often limited or noisy.This review offers a comprehensive assessment of recent developments in PINNs,combining bibliometric analysis,theoretical foundations,application-oriented insights,and methodological innovations.A biblio-metric survey indicates a rapid increase in publications on PINNs since 2018,with prominent research clusters focused on numerical methods,structural analysis,and forecasting.Building upon this trend,the review consolidates advance-ments across five principal application domains,including forward structural analysis,inverse modeling and parameter identification,structural and topology optimization,assessment of structural integrity,and manufacturing processes.These applications are propelled by substantial methodological advancements,encompassing rigorous enforcement of boundary conditions,modified loss functions,adaptive training,domain decomposition strategies,multi-fidelity and transfer learning approaches,as well as hybrid finite element–PINN integration.These advances address recurring challenges in solid mechanics,such as high-order governing equations,material heterogeneity,complex geometries,localized phenomena,and limited experimental data.Despite remaining challenges in computational cost,scalability,and experimental validation,PINNs are increasingly evolving into specialized,physics-aware tools for practical solid and structural mechanics applications.
基金Project supported by the National Natural Science Foundation of China(Nos.12172149,12172151,and 12202378)the MOE Key Laboratory of Fututer Intelligent Manufacturing Technologies for High-End Equipment of China(No.FIMFYUST-2025B07)+1 种基金the Guangzhou Municipal Bureau of Science and Technology of China(No.SL2023A04J01461)the Ministry of Science and Technology of China(No.G20221990122)。
文摘This study focuses on a new and high-efficiency approach in a unified sense of accurately simulating strength-degrading effects for geomaterials,including non-symmetric hardening-to-softening effects in tension and compression as well as non-symmetric tensile and compressive stiffness-degrading effects during unloading.It is intended to bypass both modeling and numerical complexities involved in existing approaches.To this goal,new elastoplastic equations are established with new numerical techniques.With a decoupling technique of treating tension-compression asymmetry,the foregoing complex effects are automatically incorporated as inherent response features of the new elastoplastic equations,thus bypassing usual modeling complexities.A new numerical technique of renormalizing piecewise spline functions is introduced to resolve the central yet tough issue of obtaining accurate and unified expressions for the tensile and compressive strength functions,thus bypassing usual numerical complexities and uncertainties in treating numerous unknown parameters and multiple ad hoc criteria.As such,the new approach is not only of wide applicability for various geomaterials but also of high computational efficiency with no more than three adjustable parameters.Toward validating the efficacy of the new approach,numerical examples for granite,salt rock,and sandstone-concrete combined body as well as plain concrete,high-performance concrete,and ultrahigh-performance concrete are presented by comparing model predictions with multiple data sets for strength-degrading effects in tension and compression.
基金funded by National Natural Science Foundation of China(NSFC)under Grant Nos.11702238,51904202,and 11902212Nanhu Scholars Program for Young Scholars of XYNU.
文摘This paper presents an isogeometric boundary element method(IGABEM)for transient heat conduction analysis.The Non-Uniform Rational B-spline(NURBS)basis functions,which are used to construct the geometry of the structures,are employed to discretize the physical unknowns in the boundary integral formulations of the governing equations.Bezier extraction technique is employed to accelerate the evaluation of NURBS basis functions.We adopt a radial integration method to address the additional domain integrals.The numerical examples demonstrate the advantage of IGABEM in dimension reduction and the seamless connection between CAD and numerical analysis.
基金We acknowledge the support of Defense Advanced Research Projects Agency(Grant HR00111990S2)Toyota Research Institute(Award#849910).
文摘We present two approaches to system identification, i.e. the identification of partial differentialequations (PDEs) from measurement data. The first is a regression-based variational systemidentification procedure that is advantageous in not requiring repeated forward model solves andhas good scalability to large number of differential operators. However it has strict data typerequirements needing the ability to directly represent the operators through the available data.The second is a Bayesian inference framework highly valuable for providing uncertaintyquantification, and flexible for accommodating sparse and noisy data that may also be indirectquantities of interest. However, it also requires repeated forward solutions of the PDE modelswhich is expensive and hinders scalability. We provide illustrations of results on a model problemfor pattern formation dynamics, and discuss merits of the presented methods.
基金supported under the Center for Subsurface Modeling Affiliates Program,United States of America and the National Science Foundation,United States of America(1911320,Collaborative Research:High-Fidelity Modeling of Poromechanics with Strong Discontinuities)。
文摘We present a framework that couples a high-fidelity compositional reservoir simulator with Bayesian optimization(BO)for injection well scheduling optimization in geological carbon sequestration.This work represents one of the first at tempts to apply BO and high-fidelity physics models to geological carbon storage.The implicit parallel accurate reservoir simulator(IPARS)is utilized to accurately capture the underlying physical processes during CO_(2)sequestration.IPARS provides a framework for several flow and mechanics models and thus supports both stand-alone and coupled simulations.In this work,we use the compositional flow module to simulate the geological carbon storage process.The compositional flow model,which includes a hysteretic three-phase relative permeability model,accounts for three major CO_(2)trapping mechanisms:structural trapping,residual gas trapping,and solubility trapping.Furthermore,IPARS is coupled to the International Business Machines(IBM)Corporation Bayesian Optimization Accelerator(BOA)for parallel optimizations of CO_(2)injection strategies during field-scale CO_(2)sequestration.BO builds a probabilistic surrogate for the objective function using a Bayesian machine learning algorithm-the Gaussian process regression,and then uses an acquisition function that leverages the uncertainty in the surrogate to decide where to sample.The IBM BOA addresses the three weaknesses of standard BO that limits its scalability in that IBM BOA supports parallel(batch)executions,scales better for high-dimensional problems,and is more robust to initializations.We demonstrate these merits by applying the algorithm in the optimization of the CO_(2)injection schedule in the Cranfield site in Mississippi,USA,using field data.The optimized injection schedule achieves 16%more gas storage volume and 56%less water/surfactant usage compared with the baseline.The performance of BO is compared with that of a genetic algorithm(GA)and a covariance matrix adaptation(CMA)-evolution strategy(ES).The results demonstrate the superior performance of BO,in that it achieves a competitive objective function value with over 60%fewer forward model evaluations.
基金supported by the China Postdoctoral Science Foundation(No.2020M670719)the Doctoral Research Startup Fund of Liaoning Province(No.2020-BS-066)the Fundamental Research Funds for the Central Universities(No.3132019328)。
文摘To inhibit the agglomeration of tin-based nanomaterials and simplify the complicated synthesis process,a facile and eco-friendly self-formed template method is reported to synthesize tin submicron spheres dispersed in nitrogen-doped porous carbon(Sn/NPC)by pyrolysis of a mixture of disodium stannous citrate and urea.The vital point of this strategy is the formation of Na_(2)CO_(3)templates during pyrolysis.This self-formed Na_(2)CO_(3)acts as porous templates to support the formation of NPC.The obtained NPC provides good electronic conductivity,ample defects,and more active sites.Serving as anode for Li-ion batteries,the Sn/NPC electrode obtains a stable discharge capacity of 674.1 mAh/g after 150 cycles at 0.1 A/g.Especially,a high discharge capacity of 331.2 mAh/g can be achieved after 1100 cycles at 3 A/g.Additionally,a full cell coupled with LiCoO_(2)as cathode yields a discharge capacity of 524.8 mAh/g after 150 cycles at 0.1 A/g.In-situ XRD is implemented to investigate the alloying/dealloying reaction mechanisms.Density functional theory calculation ulteriorly explicates that NPC heightens intrinsic electronic conductivity,and NPC especially pyrrolic-N and pyridinic-N doping facilitates the Li-adsorption ability.Climbing image nudged elastic band method reveals low Li~+diffusion energy barrier in presence of N atoms,which accounts for the terrific electrochemical properties of Sn/NPC electrode.
基金supported by the UC-National Lab In-Residence Graduate Fellowship Grant L21GF3606supported by a DOD National Defense Science and Engineering Graduate(NDSEG)Research Fellowship+1 种基金supported by the Laboratory Directed Research and Development program of Los Alamos National Laboratory under project numbers 20170668PRD1 and 20210213ERsupported by the NGA under Contract No.HM04762110003.
文摘Graph learning,when used as a semi-supervised learning(SSL)method,performs well for classification tasks with a low label rate.We provide a graph-based batch active learning pipeline for pixel/patch neighborhood multi-or hyperspectral image segmentation.Our batch active learning approach selects a collection of unlabeled pixels that satisfy a graph local maximum constraint for the active learning acquisition function that determines the relative importance of each pixel to the classification.This work builds on recent advances in the design of novel active learning acquisition functions(e.g.,the Model Change approach in arXiv:2110.07739)while adding important further developments including patch-neighborhood image analysis and batch active learning methods to further increase the accuracy and greatly increase the computational efficiency of these methods.In addition to improvements in the accuracy,our approach can greatly reduce the number of labeled pixels needed to achieve the same level of the accuracy based on randomly selected labeled pixels.
基金Supported by the US Department of Energy under the PSAAP Program
文摘The Burgers＇ equation with uncertain initial and boundary conditions is approximated using a Polynomial Chaos Expansion （PCE） approach where the solution is represented as a series of stochastic, orthogonal polynomials. The resulting truncated PCE system is solved using a novel numerical discretization method based on spatial derivative operators satisfying the summation by parts property and weak boundary conditions to ensure stability. The resulting PCE solution yields an accurate quantitative description of the stochastic evolution of the system, provided that appropriate boundary conditions are available. The specification of the boundary data is shown to influence the solution; we will discuss the problematic implications of the lack of precisely characterized boundary data and possible ways of imposing stable and accurate boundary conditions.
基金FF and LDL gratefully acknowledge the financial support of the German Research Foundation(DFG)within the DFG Priority Program SPP 1748“Reliable Simulation Techniques in Solid Mechanics”.AR has been partially supported by the MIUR-PRIN project XFAST-SIMS(No.20173C478 N).
文摘We investigate primal and mixed u−p isogeometric collocation methods for application to nearly-incompressible isotropic elasticity.The primal method employs Navier’s equations in terms of the displacement unknowns,and the mixed method employs both displacement and pressure unknowns.As benchmarks for what might be considered acceptable accuracy,we employ constant-pressure Abaqus finite elements that are widely used in engineering applications.As a basis of comparisons,we present results for compressible elasticity.All the methods were completely satisfactory for the compressible case.However,results for low-degree primal methods exhibited displacement locking and in general deteriorated in the nearly-incompressible case.The results for the mixed methods behaved very well for two of the problems we studied,achieving levels of accuracy very similar to those for the compressible case.The third problem,which we consider a“torture test”presented a more complex story for the mixed methods in the nearly-incompressible case.
基金the German Science Foundation(DFG)for supportFuyao University of Science and Technology of Fujian,China+1 种基金supported by the National Natural Science Foundation of China(Grant Nos.12172149 and 12172151)the Ministry of Science and Technology of China(Grant No.G20221990122)。
文摘New and perhaps unexpected progress in rate-independent elastoplastic modeling is reported with a unified approach toward simulating widely ranging non-elastic effects of various advanced engineering materials such as metals,shape memory alloys,granular materials,fiber-reinforced composites,as well as crystalline solids,etc.This progress originates from a simple idea of bypassing inherent limitations of usual elastoplastic formulations centered on the notion of yielding.With no reference to any yield criteria,the plastic strain-rate should be induced at all stress levels in a more realistic sense that it is small for stresses within a classical yield surface and becomes appreciable for stresses close to and on this surface.A new and unified flow rule for the plastic strain-rate is then proposed of the same smooth form for all cases of both the stress level and the stress rate.Without imposing the ad hoc simplified conditions introduced in usual Prandtl-Reuss equations,new elastoplastic equations are then established by incorporating such small deviations from realistic behaviors as neglected just by postulating these conditions.It turns out that the new equations are not only essentially simpler in both conceptual and structural formulations,but can automatically as inherent response features incorporate significant effects excluded from usual Prandtl-Reuss equations,such as the yielding and unloading behaviors with smooth transitions,the pseudo-elastic effect with hysteresis loops,the non-elastic recovery during unloading as well as failure effects under either monotone or cyclic loading conditions,etc.Since such effects not only go beyond the scope of usual elastoplastic equations but can be only partially simulated even if augmented constitutive equations are postulated toward further characterizing damaging and fracturing effects resulting from evolving micro-defects and macro-cracks,it may be probably surprising that now the new equations of essentially simpler structure not only can in a unified manner simulate all these effects but also can bypass numerical complexities in integrating various rate constitutive equations of complex structures.New results in treating long-standing issues in a few respects are presented,including(i)the yielding and the unloading behaviors with smooth transitions,(ii)the non-elastic recovery during unloading,(iii)the pseudo-elastic effect as extraordinary Bauschinger effect,(iv)failure effects under monotone and cyclic loading,(v)anisotropic multi-mode failure effects of unidirectional composites,(vi)new formulation of crystal elastoplasticity without involving non-uniqueness and singularity issues,(vii)non-normality effects for non-proportional multi-axial loading cases,and(viii)high efficiency algorithms for simulating multi-axial fatigue effects.
基金funded by the Guangdong Basic and Applied Basic Research Foundation(2024A1515011786 and 2025A1515010672).
文摘Topology optimization(TO)has become a core computational paradigm for structural design by defining optimality through physics-based objectives and constraints.However,practical engineering design often involves incomplete and imperfect physical modeling due to multi-physics coupling,manufacturing uncertainty,and computational constraints,leaving critical design factors insufficiently captured in purely physics-driven formulations.In parallel,data-driven and generative methods have enabled rapid topology generation and intent-aware design exploration,yet often weaken explicit optimality guarantees.This review argues that these seemingly divergent developments can be organized under a unified information-physics perspective.We term this emerging field Topology Optimization Informatics(TOI):optimal structural design is obtained through the joint modeling and optimization of physical laws and design-relevant information.We first summarize the integration of artificial intelligence(AI)and TO into two major paradigms:AI-based one-shot TO,which learns mappings or distributions of near-optimal designs from data and prioritizes fast generation and diversity,and AI-enhanced iterative TO,which embeds learning-based modules into the classical solver-in-the-loop pipeline while keeping the underlying governing equations unchanged.Finally,we show that traditionally separate tasks—design control,computational acceleration,and fidelity enhancement—can be interpreted as different manifestations of information-physics co-modeling within a single optimization framework,thereby clarifying their connections and design implications and outlining opportunities for semantic-and data-enabled next-generation structural design.
基金supported by the Natural Science Foundation of Jiangsu Province(Grant No.BK20180855)Research Fund of State Key Laboratory of Mechanics and Control of Mechanical Structures(Grant No.MCMS-E-0219Y01)Research and Practice Innovation Program of postgraduates in Jiangsu Province(Grant No.KYCX20-3076)。
文摘Potential damage in composite structures caused by hail ice impact is an essential safety threat to the aircraft in flight.In this study,a nonlinear finite element(FE)model is developed to investigate the dynamic response and damage behavior of hybrid corrugated sandwich structures subjected to high velocity hail ice impact.The impact and breaking behavior of hail are described using the FE-smoothed particle hydrodynamics(FE-SPH)method.A rate-dependent progressive damage model is employed to capture the intra-laminar damage response;cohesive element and surface-based cohesive contact are implemented to predict the inter-laminar delamination and sheet/core debonding phenomena respectively.The transient processes of sandwich structure under different hail ice impact conditions are analyzed.Comparative analysis is conducted to address the influences of core shape and impact position on the impact performance of sandwich structures and the corresponding energy absorption characteristics are also revealed.
基金This study was funded by the National Natural Science Foundation of China(NSFC)(Grant Nos.11702238,51904202 and 11902212)and Nanhu Scholars Program for Young Scholars of XYNU.
文摘The paper applied the isogeometric boundary element method(IGABEM)to thermoelastic problems.The Non-Uniform Rational B-splines(NURBS)used to construct geometric models are employed to discretize the boundary integral formulation of the governing equation.Due to the existence of thermal stress,the domain integral term appears in the boundary integral equation.We resolve this problem by incorporating radial integration method into IGABEM which converts the domain integral to the boundary integral.In this way,IGABEM can maintain its advantages in dimensionality reduction and more importantly,seamless integration of CAD and numerical analysis based on boundary representation.The algorithm is verified by numerical examples.
基金We acknowledge the support of the National Natural Science Foundation of China(NSFC)under Grant Nos.51904202 and 11702238Stephane Bordas thanks the financial support of Intuitive modeling and SIMulation platform(IntuiSIM)(PoC17/12253887)grant by Luxembourg National Research Fund.
文摘This paper proposes a novel optimization framework in passive control techniques to reduce noise pollution.The geometries of the structures are represented by Catmull-Clark subdivision surfaces,which are able to build gap-free Computer-Aided Design models and meanwhile tackle the extraordinary points that are commonly encountered in geometricmodelling.The acoustic fields are simulated using the isogeometric boundary elementmethod,and a density-based topology optimization is conducted to optimize distribution of sound-absorbing materials adhered to structural surfaces.The approach enables one to perform acoustic optimization from Computer-Aided Design models directly without needingmeshing and volume parameterization,thereby avoiding the geometric errors and time-consuming preprocessing steps in conventional simulation and optimization methods.The effectiveness of the present method is demonstrated by three dimensional numerical examples.
文摘During April 20-22,2022,colleagues and friends gathered at the Institute of Pure&Applied Mathematics(IPAM),at the University of California at Los Angeles to celebrate Professor Stanley Osher's 8Oth birthday in a conference focusing on recent developments in"Optimization,Shape analysis,High-dimensional differential equations in science and Engineering,and machine learning Research(OSHER)"This conference hosted in-person talks by mathematicians,scientists,and industrial professionals worldwide.Those who could not attend extended their warm regards and expressed their appreciation for Professor Osher.
基金the National Natural Science Foundation of China(NSFC)under Grant Nos.12272124 and 11972146.
文摘Isogeometric analysis (IGA) is known to showadvanced features compared to traditional finite element approaches.Using IGA one may accurately obtain the geometrically nonlinear bending behavior of plates with functionalgrading (FG). However, the procedure is usually complex and often is time-consuming. We thus put forward adeep learning method to model the geometrically nonlinear bending behavior of FG plates, bypassing the complexIGA simulation process. A long bidirectional short-term memory (BLSTM) recurrent neural network is trainedusing the load and gradient index as inputs and the displacement responses as outputs. The nonlinear relationshipbetween the outputs and the inputs is constructed usingmachine learning so that the displacements can be directlyestimated by the deep learning network. To provide enough training data, we use S-FSDT Von-Karman IGA andobtain the displacement responses for different loads and gradient indexes. Results show that the recognition erroris low, and demonstrate the feasibility of deep learning technique as a fast and accurate alternative to IGA formodeling the geometrically nonlinear bending behavior of FG plates.
文摘Anti-plane deformation of square lattices containing interphases is analyzed. It is assumed that lattices are linear elastic but not necessarily isotropic, whereas interphases exhibit non-linear elastic behavior. It is demonstrated that such problems can be treated effectively using Green＇s functions, which allow to eliminate the degrees of freedom outside of the interphase. Illustrative numerical examples focus on the determination of applied stresses leading to lattice instability.
基金supported by the National Science Foundation(Grant No.DMS-1440415)partially supported by a grant from the Simons Foundation,NSF Grants DMS-1720171 and DMS-2110895a Discovery Grant from Natural Sciences and Engineering Research Council of Canada.
文摘We propose a new framework for the sampling,compression,and analysis of distributions of point sets and other geometric objects embedded in Euclidean spaces.Our approach involves constructing a tensor called the RaySense sketch,which captures nearest neighbors from the underlying geometry of points along a set of rays.We explore various operations that can be performed on the RaySense sketch,leading to different properties and potential applications.Statistical information about the data set can be extracted from the sketch,independent of the ray set.Line integrals on point sets can be efficiently computed using the sketch.We also present several examples illustrating applications of the proposed strategy in practical scenarios.
基金supported by the National Key R&D Program of China(Grant No.2021YFA1400201)the National Natural Science Foundation of China(Grant Nos.12025407 and 11934004)Chinese Academy of Sciences(Grant Nos.XDB330301 and YSBR047)。
文摘Light-induced ultrafast spin dynamics in materials is of great importance for developments of spintronics and magnetic storage technology.Recent progresses include ultrafast demagnetization,magnetic switching,and magnetic phase transitions,while the ultrafast generation of magnetism is hardly achieved.Here,a strong lightinduced magnetization(up to 0.86μBper formula unit)is identified in non-magnetic monolayer molybdenum disulfide(MoS_(2)).With the state-of-the-art time-dependent density functional theory simulations,we demonstrate that the out-of-plane magnetization can be induced by circularly polarized laser,where chiral phonons play a vital role.The phonons strongly modulate spin-orbital interactions and promote electronic transitions between the two conduction band states,achieving an effective magnetic field~380 T.Our study provides important insights into the ultrafast magnetization and spin-phonon coupling dynamics,facilitating effective light-controlled valleytronics and magnetism.
