1 citations · 2 across the 6 of their papers we have counts for
6 papers
OmniRemesh: Adaptive and Quasi-differentiable Remeshing for Crystal Plasticity Simulation and Inverse Parameter Calibration under Large Deformation
Ningyu Yan, Yuntong Huang, Yang Xiang
Large-deformation crystal plasticity finite element method (CPFEM) simulations are often limited by accumulated mesh distortion, which degrades accuracy and numerical stability, wh…
Prescribed-Basis Coefficient-to-Coefficient Neural Operator for Partial Differential Equations
Chuqi Chen, Yang Xiang, Weihong Zhang
Operator learning provides a data-driven approach to approximating solution operators of partial differential equations, but its effectiveness depends strongly on how input and out…
Learn Singularly Perturbed Solutions via Homotopy Dynamics
Chuqi Chen, Yahong Yang, Yang Xiang +1
Solving partial differential equations (PDEs) using neural networks has become a central focus in scientific machine learning. Training neural networks for singularly perturbed pro…
Quantifying Training Difficulty and Accelerating Convergence in Neural Network-Based PDE Solvers
Chuqi Chen, Qixuan Zhou, Yahong Yang +2
Neural network-based methods have emerged as powerful tools for solving partial differential equations (PDEs) in scientific and engineering applications, particularly when handling…
Investigating amorphization as a deformation mechanism using a novel phase field model at the mesoscale
Yuntong Huang, Shuyang Dai, Chuqi Chen +1
Amorphization during severe plastic deformation has been observed in various crystalline materials, yet its underlying mechanisms remain poorly understood. This study introduces a…
Automatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations
Chuqi Chen, Yahong Yang, Yang Xiang +1
Neural network-based approaches have recently shown significant promise in solving partial differential equations (PDEs) in science and engineering, especially in scenarios featuri…