activity
20232026
most citedThe Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory

9 citations · 9 across the 5 of their papers we have counts for

collaborators
Showing cs.LGShow all

2 papers · 1 filter

cs.LG2025

CODE: A global approach to ODE dynamics learning

Nils Wildt, Daniel M. Tartakovsky, Sergey Oladyshkin +1

Ordinary differential equations (ODEs) are a conventional way to describe the observed dynamics of physical systems. Scientists typically hypothesize about dynamical behavior, prop…

cs.LG2024

Inferring Underwater Topography with FINN

Coşku Can Horuz, Matthias Karlbauer, Timothy Praditia +3

Spatiotemporal partial differential equations (PDEs) find extensive application across various scientific and engineering fields. While numerous models have emerged from both physi…