Neural operator architectures employ neural networks to approximate oper...
This paper provides a comprehensive error analysis of learning with
vect...
Coupled oscillators are being increasingly used as the basis of machine
...
Neural operator architectures approximate operators between
infinite-dim...
PCA-Net is a recently proposed neural operator architecture which combin...
A large class of hyperbolic and advection-dominated PDEs can have soluti...
We study the well-posedness of Bayesian inverse problems for PDEs, for w...
Fourier neural operators (FNOs) have recently been proposed as an effect...
We derive bounds on the error, in high-order Sobolev norms, incurred in ...
DeepOnets have recently been proposed as a framework for learning nonlin...
The question of energy concentration in approximate solution sequences
u...
We propose and study the framework of dissipative statistical solutions ...