5 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.LG2024
What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications
Emily Williams, Amanda Howard, Brek Meuris +1
Physics-informed deep operator networks (DeepONets) have emerged as a promising approach toward numerically approximating the solution of partial differential equations (PDEs). In…
math.NA2021
Machine-learning custom-made basis functions for partial differential equations
Brek Meuris, Saad Qadeer, Panos Stinis
Spectral methods are an important part of scientific computing's arsenal for solving partial differential equations (PDEs). However, their applicability and effectiveness depend cr…
physics.flu-dyn2021★ 5 cited
Optimal renormalization of multi-scale systems
Jacob Price, Brek Meuris, Madelyn Shapiro +1
While model order reduction is a promising approach in dealing with multi-scale time-dependent systems that are too large or too expensive to simulate for long times, the resulting…