Rapid and reliable onboard optimization of bank angle profiles is crucial for mitigating uncertainties during Mars atmospheric entry.This paper presents a neural-network-accelerated methodology for optimizing parametr...Rapid and reliable onboard optimization of bank angle profiles is crucial for mitigating uncertainties during Mars atmospheric entry.This paper presents a neural-network-accelerated methodology for optimizing parametric bank angle profiles in Mars atmospheric entry missions.The methodology includes a universal approach to handling path constraints and a reliable solution method based on the Particle Swarm Optimization(PSO)algorithm.For illustrative purposes,a mission with the objective of maximizing terminal altitude is considered.The original entry optimization problem is converted into optimizing three coefficients for the bank angle profiles with terminal constraints by formulating a parametric Mars entry bank angle profile and constraint handling methods.The parameter optimization problem is addressed using the PSO algorithm,with reliability enhanced by increasing the PSO swarm size.To improve computational efficiency,an enhanced Deep Operator Network(Deep ONet)is used as a dynamics solver to predict terminal states under various bank angle profiles rapidly.Numerical simulations demonstrate that the proposed methodology ensures reliable convergence with a sufficiently large PSO swarm while maintaining high computational efficiency facilitated by the neural-network-based dynamics solver.Compared to the existing methodologies,this methodology offers a streamlined process,the reduced sensitivity to initial guesses,and the improved computational efficiency.展开更多
The integration of physics-based modelling and data-driven artificial intelligence(AI)has emerged as a transformative paradigm in computational mechanics.This perspective reviews the development and current status of ...The integration of physics-based modelling and data-driven artificial intelligence(AI)has emerged as a transformative paradigm in computational mechanics.This perspective reviews the development and current status of AI-empowered frameworks,including data-driven methods,physics-informed neural networks,and neural operators.While these approaches have demonstrated significant promise,challenges remain in terms of robustness,generalisation,and computational efficiency.We delineate four promising research directions:(1)Modular neural architectures inspired by traditional computational mechanics,(2)physics informed neural operators for resolution-invariant operator learning,(3)intelligent frameworks for multiphysics and multiscale biomechanics problems,and(4)structural optimisation strategies based on physics constraints and reinforcement learning.These directions represent a shift toward foundational frameworks that combine the strengths of physics and data,opening new avenues for the modelling,simulation,and optimisation of complex physical systems.展开更多
Transformer has achieved remarkable results in various fields,including its application in modeling dynamic systems governed by partial differential equations.However,transformer still face challenges in achieving lon...Transformer has achieved remarkable results in various fields,including its application in modeling dynamic systems governed by partial differential equations.However,transformer still face challenges in achieving long-term stable predictions for three-dimensional turbulence.In this paper,we propose an implicit factorized transformer(IFactFormer)model,which enables stable training at greater depths through implicit iteration over factorized attention.IFactFormer is applied to large eddy simulation of three-dimensional homogeneous isotropic turbulence(HIT),and is shown to be more accurate than the FactFormer,Fourier neural operator,and dynamic Smagorinsky model(DSM)in the prediction of the velocity spectra,probability density functions of velocity increments and vorticity,temporal evolutions of velocity and vorticity root-mean-square value and isosurface of the normalized vorticity.IFactFormer can achieve long-term stable predictions of a series of turbulence statistics in HIT.Fur-thermore,IFactFormer showcases superior computational efficiency compared to the conventional DSM in large eddy simulation.展开更多
Operant conditioning is one of the fundamental mechanisms of animal learning, which suggests that the behavior of all animals, from protists to humans, is guided by its consequences. We present a new stochastic learni...Operant conditioning is one of the fundamental mechanisms of animal learning, which suggests that the behavior of all animals, from protists to humans, is guided by its consequences. We present a new stochastic learning automaton called a Skinner au- tomaton that is a psychological model for formalizing the theory of operant conditioning. We identify animal operant learning with a thermodynamic process, and derive a so-called Skinner algorithm from Monte Carlo method as well as Metropolis algo- rithm and simulated annealing. Under certain conditions, we prove that the Skinner automaton is expedient, 6-optimal, optimal, and that the operant probabilities converge to the set of stable roots with probability of 1. The Skinner automaton enables ma- chines to autonomously learn in an animal-like way.展开更多
基金supported in part by the National Defense Basic Scientific Research Program of China(No.JCKY2021603B030)the Shenzhen Fundamental Research Program,China(No.JCYJ20220818102601004)the Science Center Program of National Natural Science Foundation of China(No.62188101)。
文摘Rapid and reliable onboard optimization of bank angle profiles is crucial for mitigating uncertainties during Mars atmospheric entry.This paper presents a neural-network-accelerated methodology for optimizing parametric bank angle profiles in Mars atmospheric entry missions.The methodology includes a universal approach to handling path constraints and a reliable solution method based on the Particle Swarm Optimization(PSO)algorithm.For illustrative purposes,a mission with the objective of maximizing terminal altitude is considered.The original entry optimization problem is converted into optimizing three coefficients for the bank angle profiles with terminal constraints by formulating a parametric Mars entry bank angle profile and constraint handling methods.The parameter optimization problem is addressed using the PSO algorithm,with reliability enhanced by increasing the PSO swarm size.To improve computational efficiency,an enhanced Deep Operator Network(Deep ONet)is used as a dynamics solver to predict terminal states under various bank angle profiles rapidly.Numerical simulations demonstrate that the proposed methodology ensures reliable convergence with a sufficiently large PSO swarm while maintaining high computational efficiency facilitated by the neural-network-based dynamics solver.Compared to the existing methodologies,this methodology offers a streamlined process,the reduced sensitivity to initial guesses,and the improved computational efficiency.
基金supported by the Australian Research Council(Grant No.IC190100020)the Australian Research Council Indus〓〓try Fellowship(Grant No.IE230100435)the National Natural Science Foundation of China(Grant Nos.12032014 and T2488101)。
文摘The integration of physics-based modelling and data-driven artificial intelligence(AI)has emerged as a transformative paradigm in computational mechanics.This perspective reviews the development and current status of AI-empowered frameworks,including data-driven methods,physics-informed neural networks,and neural operators.While these approaches have demonstrated significant promise,challenges remain in terms of robustness,generalisation,and computational efficiency.We delineate four promising research directions:(1)Modular neural architectures inspired by traditional computational mechanics,(2)physics informed neural operators for resolution-invariant operator learning,(3)intelligent frameworks for multiphysics and multiscale biomechanics problems,and(4)structural optimisation strategies based on physics constraints and reinforcement learning.These directions represent a shift toward foundational frameworks that combine the strengths of physics and data,opening new avenues for the modelling,simulation,and optimisation of complex physical systems.
基金supported by the National Natural Science Foundation of China(Grant No.12172161)the NSFC Basic Science Center Program(Grant No.11988102)+1 种基金the Shenzhen Science and Technology Program(Grant No.KQTD20180411143441009)supported by Center for Computational Science and Engineering of Southern University of Science and Technology.
文摘Transformer has achieved remarkable results in various fields,including its application in modeling dynamic systems governed by partial differential equations.However,transformer still face challenges in achieving long-term stable predictions for three-dimensional turbulence.In this paper,we propose an implicit factorized transformer(IFactFormer)model,which enables stable training at greater depths through implicit iteration over factorized attention.IFactFormer is applied to large eddy simulation of three-dimensional homogeneous isotropic turbulence(HIT),and is shown to be more accurate than the FactFormer,Fourier neural operator,and dynamic Smagorinsky model(DSM)in the prediction of the velocity spectra,probability density functions of velocity increments and vorticity,temporal evolutions of velocity and vorticity root-mean-square value and isosurface of the normalized vorticity.IFactFormer can achieve long-term stable predictions of a series of turbulence statistics in HIT.Fur-thermore,IFactFormer showcases superior computational efficiency compared to the conventional DSM in large eddy simulation.
基金supported by the National Natural Science Foundation of China(Grant Nos.61075110,60774077,61375086)the National Basic Research Program of China("973" Project)(Grant No.2012CB720000)+3 种基金the National High-Tech Research and Development Program of China("863" Project)(Grant No.2007AA04Z226)the Beijing Natural Science Foundation(Grant No.4102011)the Key Project of S&T Plan of Beijing Municipal Commission of Education(Grant Nos.KM2008-10005016,KZ201210005001)the Specialized Research Fund for the Doctoral Program of Higher Education(Grant No.20101103110007)
文摘Operant conditioning is one of the fundamental mechanisms of animal learning, which suggests that the behavior of all animals, from protists to humans, is guided by its consequences. We present a new stochastic learning automaton called a Skinner au- tomaton that is a psychological model for formalizing the theory of operant conditioning. We identify animal operant learning with a thermodynamic process, and derive a so-called Skinner algorithm from Monte Carlo method as well as Metropolis algo- rithm and simulated annealing. Under certain conditions, we prove that the Skinner automaton is expedient, 6-optimal, optimal, and that the operant probabilities converge to the set of stable roots with probability of 1. The Skinner automaton enables ma- chines to autonomously learn in an animal-like way.
