Scientific machine learning through physics–informed neural networks: Where we are and what's next
Abstract Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode
model equations, like Partial Differential Equations (PDE), as a component of the neural …
model equations, like Partial Differential Equations (PDE), as a component of the neural …
Neural operators for accelerating scientific simulations and design
Scientific discovery and engineering design are currently limited by the time and cost of
physical experiments. Numerical simulations are an alternative approach but are usually …
physical experiments. Numerical simulations are an alternative approach but are usually …
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
Physics-informed neural networks (PINNs) have shown to be effective tools for solving both
forward and inverse problems of partial differential equations (PDEs). PINNs embed the …
forward and inverse problems of partial differential equations (PDEs). PINNs embed the …
Respecting causality is all you need for training physics-informed neural networks
S Wang, S Sankaran, P Perdikaris - ar** strategies for physics-informed neural networks (PINNs) and their temporal decompositions
Physics-informed neural networks (PINNs) as a means of solving partial differential
equations (PDE) have garnered much attention in the Computational Science and …
equations (PDE) have garnered much attention in the Computational Science and …