1 citations · 2 across the 9 of their papers we have counts for
9 papers
From Fixed Grids to Moving Particles:A Transferable Latent Operator for Fluid Dynamics
Meng Li, Chuqi Chen, Zhengqing Gao +4
Lagrangian modeling is vital to fluid dynamics, as it characterizes particle transport and complements the Eulerian representation. However, Lagrangian trajectories are less common…
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…
SSBE-PINN: A Sobolev Boundary Scheme Boosting Stability and Accuracy in Elliptic/Parabolic PDE Learning
Qixuan Zhou, Chuqi Chen, Tao Luo +1
Physics-Informed Neural Networks (PINNs) have emerged as a powerful framework for solving partial differential equations (PDEs), yet they often fail to achieve accurate convergence…
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…
Stability Analysis Framework for Particle-based Distance GANs with Wasserstein Gradient Flow
Chuqi Chen, Yue Wu, Yang Xiang
In this paper, we investigate the training process of generative networks that use a type of probability density distance named particle-based distance as the objective function, e…
Elastic Interaction Energy-Based Generative Model: Approximation in Feature Space
Chuqi Chen, Yue Wu, Yang Xiang
In this paper, we propose a novel approach to generative modeling using a loss function based on elastic interaction energy (EIE), which is inspired by the elastic interaction betw…