21 citations · 21 across the 7 of their papers we have counts for
4 papers · 1 filter
Derivative-informed Graph Convolutional Autoencoder with Phase Classification for the Lifshitz-Petrich Model
Yanlai Chen, Yajie Ji, Zhenli Xu
The Lifshitz-Petrich (LP) model is a classical model for describing complex spatial patterns such as quasicrystals and multiphase structures. Solving and classifying the solutions…
SGPT-PINNs: Sparse and Small models for PDEs
Yajie Ji, Yanlai Chen, Shawn Koohy
We propose SGPT-PINN, a sparse and small model for solving parametric partial differential equations (PDEs). Similar to Small Language Models (SLMs), SGPT-PINN is tailored…
Machine-Learning Interatomic Potentials for Long-Range Systems
Yajie Ji, Jiuyang Liang, Zhenli Xu
Machine-learning interatomic potentials have emerged as a revolutionary class of force-field models in molecular simulations, delivering quantum-mechanical accuracy at a fraction o…
EGPT-PINN: Entropy-enhanced Generative Pre-Trained Physics Informed Neural Networks for parameterized nonlinear conservation laws
Yajie Ji, Yanlai Chen, Zhenli Xu
We propose an entropy-enhanced Generative Pre-Trained Physics-Informed Neural Network with a transform layer (EGPT-PINN) for solving parameterized nonlinear conservation laws. The…