1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2026
Physics-informed neural operator for predictive parametric phase-field modelling
Nanxi Chen, Airong Chen, Rujin Ma
Predicting the microstructural and morphological evolution of materials through phase-field modelling is computationally intensive, particularly for high-throughput parametric stud…
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…