19 citations · 21 across the 2 of their papers we have counts for
3 papers
An optimal Petrov-Galerkin framework for operator networks
Philip Charles, Deep Ray, Yue Yu +7
The optimal Petrov-Galerkin formulation to solve partial differential equations (PDEs) recovers the best approximation in a specified finite-dimensional (trial) space with respect…
Neural Green's Operators for Parametric Partial Differential Equations
Hugo Melchers, Joost Prins, Michael Abdelmalik
This work introduces a paradigm for constructing parametric neural operators that are derived from finite-dimensional representations of Green's operators for linear partial differ…
Towards learning Lattice Boltzmann collision operators
Alessandro Corbetta, Alessandro Gabbana, Vitaliy Gyrya +3
In this work we explore the possibility of learning from data collision operators for the Lattice Boltzmann Method using a deep learning approach. We compare a hierarchy of designs…