Diffeomorphic Latent Neural Operators for Data-Efficient Learning of
Solutions to Partial Differential Equations
Diffeomorphic Latent Neural Operators for Data-Efficient Learning of
Solutions to Partial Differential Equations
A computed approximation of the solution operator to a system of partial differential equations (PDEs) is needed in various areas of science and engineering. Neural operators have been shown to be quite effective at predicting these solution generators after training on high-fidelity ground truth data (e.g. numerical simulations). However, in …