14 papers
POD-accelerated high-order multiscale method with corrected-basis reconstruction for transient multi-continuum problems
Jiayuan Peng, Hao Dong, Jiale Linghu
Multi-continuum models provide an effective macroscopic description of transient flow in fractured and highly heterogeneous porous media by introducing several interacting state va…
Efficient higher-order multi-scale method and its convergence estimate for dynamic nonlinear hygro-thermo-mechanical coupling problems of heterogeneous structures
Yifei Ding, Hao Dong, Jiale Linghu +1
This paper presents a novel higher-order multi-scale (HOMS) computational framework for efficient, high-accuracy, and low-cost simulation of nonlinear hygro-thermo-mechanical (H-T-…
A high-order multi-scale method and its convergence analysis for temperature-dependent nonlinear thermal radiation problems of composite structures
Hao Dong, Yongfei Hu, Jiale Linghu +1
Accurate prediction of the nonlinear radiation thermal transfer in composite structures with temperature-dependent properties is significant in high-temperature applications of the…
A Structure-Adaptive Random Feature Method for High-Dimensional Elliptic PDEs
Jiale Linghu, Hao Dong, Yangshuai Wang
Random-feature methods reduce high-dimensional elliptic PDE collocation to linear coefficient problems, but full-dimensional trial spaces overlook lower-dimensional structure. We i…
Residual-Christoffel Sampling for Random Feature Collocation of Linear PDEs
Jiale Linghu, Yangshuai Wang
Random feature collocation fixes a randomly generated trial space and determines its coefficients from a linear least-squares system. Stability then depends on whether the sampled…
Trainable Photonic Measurement for Physics-Informed PDE Learning
Jiale Linghu, Hao Dong, Yangshuai Wang
Photonic quantum machine learning offers a route to trainable physical representations built from phase, interference and measurement. However, its role in scientific machine learn…
Random-Feature Kalman Filtering for Linear PDE Data Assimilation
Xi'an Li, Jiale Linghu, Yangshuai Wang
Data assimilation for time-dependent partial differential equations (PDEs) requires Bayesian updates of an evolving field from streaming, sparse, and noisy observations, while keep…
Liquid Random Feature Methods for Time-Dependent Partial Differential Equations
Jiale Linghu, Yangshuai Wang
A central challenge in mesh-free space--time approximation for time-dependent partial differential equations is to represent evolving temporal scales while keeping residual minimiz…
Local Surrogates for Harmonic Vibrational Entropy in Multilattices
Tina Torabi, Jiale Linghu, Yangshuai Wang
Harmonic vibrational entropy is a key finite-temperature contribution to defect thermodynamics, but direct evaluation by dense Hessian diagonalization scales cubically with atom co…
A higher-order three-scale computational method for efficient nonlinear thermo-mechanical coupling simulation of heterogeneous structures with multiple spatial scales
Hao Dong, Yanqi Wang, Jiale Linghu +1
Classical multi-scale methods involving two spatial scales face significant challenges when simulating heterogeneous structures with complicated three-scale spatial configurations.…
Higher-order multi-scale computational method and its convergence analysis for hygro-thermo-mechanical coupling problems of quasi-periodic composite structures
Hao Dong, Yifei Ding, Jiale Linghu +2
This paper proposes a novel higher-order multi-scale (HOMS) computational method, which is highly targeted for efficient, high-accuracy and low-computational-cost simulation of hyg…
SO-PIFRNN: Self-optimization physics-informed Fourier-features randomized neural network for solving partial differential equations
Jiale Linghu, Weifeng Gao, Hao Dong +1
This study proposes a self-optimization physics-informed Fourier-features randomized neural network (SO-PIFRNN) framework, which significantly improves the numerical solving accura…
Self-optimization wavelet-learning method for predicting nonlinear thermal conductivity of highly heterogeneous materials with randomly hierarchical configurations
Jiale Linghu, Hao Dong, Weifeng Gao +1
In the present work, we propose a self-optimization wavelet-learning method (SO-W-LM) with high accuracy and efficiency to compute the equivalent nonlinear thermal conductivity of…
Higher-order multi-scale deep Ritz method for multi-scale problems of authentic composite materials
Jiale Linghu, Hao Dong, Junzhi Cui +1
The direct deep learning simulation for multi-scale problems remains a challenging issue. In this work, a novel higher-order multi-scale deep Ritz method (HOMS-DRM) is developed fo…