4 papers
On the training of physics-informed neural operators for solving parametric partial differential equations
Nanxi Chen, Chuanjie Cui, Airong Chen +2
Physics-informed neural operators (PINOs) aim to learn solution operators for partial differential equations by using the governing physics as supervision, rather than relying sole…
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…
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-…
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…