Neural Conservation Laws: A Divergence-Free Perspective

4 October 2022

Papers citing "Neural Conservation Laws: A Divergence-Free Perspective"

7 / 7 papers shown

Title
Learning and Transferring Physical Models through Derivatives Alessandro Trenta Andrea Cossu Davide Bacciu AI4CE 26 0 0 02 May 2025
Fast Dynamic 1D Simulation of Divertor Plasmas with Neural PDE Surrogates Y. Poels G. Derks E. Westerhof Koen Minartz Sven Wiesen Vlado Menkovski 3DGS AI4CE 6 15 0 30 May 2023
Exact conservation laws for neural network integrators of dynamical systems E. Müller PINN 26 12 0 23 Sep 2022
Generative Modeling with Optimal Transport Maps Litu Rout Alexander Korotin Evgeny Burnaev OT DiffM 114 56 0 06 Oct 2021
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization Chin-Wei Huang Ricky T. Q. Chen Christos Tsirigotis Aaron Courville OT 99 95 0 10 Dec 2020
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 228 3,202 0 24 Nov 2016
Input Convex Neural Networks Brandon Amos Lei Xu J. Zico Kolter 163 496 0 22 Sep 2016