222 citations · 358 across the 7 of their papers we have counts for
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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.CE2025
On the fracture mechanics validity of small scale tests
C. Cui, L. Cupertino-Malheiros, Z. Xiong +1
There is growing interest in conducting small-scale tests to gain additional insight into the fracture behaviour of components across a wide range of materials. For example, micro-…
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…