The recent surge in large-scale foundation models has spurred the develo...
Partial differential equations (PDEs) underlie our understanding and
pre...
Physics-informed neural networks (PINNs) have recently emerged as promis...
In this paper, we perform the convergence analysis of unsupervised
Legen...
Physics-informed neural networks (PINNs) have emerged as new data-driven...
Normalizing flows model probability distributions by learning invertible...
We propose a new semi-analytic physics informed neural network (PINN) to...
With the increases in computational power and advances in machine learni...
Machine learning methods have been lately used to solve differential
equ...
Nonlinear differential equations are challenging to solve numerically an...
Deep neural networks have achieved state-of-the-art performance in a var...
We consider the optimal experimental design problem for an uncertain Kur...