135 citations · 136 across the 3 of their papers we have counts for
3 papers
cs.LG2025
Enforcing hidden physics in physics-informed neural networks
Nanxi Chen, Sifan Wang, Rujin Ma +2
Physics-informed neural networks (PINNs) represent a new paradigm for solving partial differential equations (PDEs) by integrating physical laws into the learning process of neural…
cs.LG2025★ 1 cited
Sharp-PINNs: staggered hard-constrained physics-informed neural networks for phase field modelling of corrosion
Nanxi Chen, Chuanjie Cui, Rujin Ma +2
Physics-informed neural networks have shown significant potential in solving partial differential equations (PDEs) across diverse scientific fields. However, their performance ofte…
cond-mat.mtrl-sci2022★ 135 cited
A generalised, multi-phase-field theory for dissolution-driven stress corrosion cracking and hydrogen embrittlement
C. Cui, R. Ma, E. Martínez-Pañeda
We present a phase field-based electro-chemo-mechanical formulation for modelling mechanics-enhanced corrosion and hydrogen-assisted cracking in elastic-plastic solids. A multi-pha…