Physics informed neural networks (PINNs) require regularity of solutions...
We derive rigorous bounds on the error resulting from the approximation ...
Existing architectures for operator learning require that the number and...
We propose a very general framework for deriving rigorous bounds on the
...
We prove rigorous bounds on the errors resulting from the approximation ...
Physics informed neural networks approximate solutions of PDEs by minimi...
We derive bounds on the error, in high-order Sobolev norms, incurred in ...
Change point detection (CPD) aims to locate abrupt property changes in t...
Deep neural networks and the ENO procedure are both efficient frameworks...