Discovering Conservation Laws using Optimal Transport and Manifold Learning

31 August 2022

Papers citing "Discovering Conservation Laws using Optimal Transport and Manifold Learning"

8 / 8 papers shown

Title
AI Poincaré 2.0: Machine Learning Conservation Laws from Differential Equations Ziming Liu Varun Madhavan M. Tegmark PINN 64 28 0 23 Mar 2022
Manifold learning with arbitrary norms Joe Kileel Amit Moscovich Nathan Zelesko A. Singer 86 26 0 28 Dec 2020
Discovering Symmetry Invariants and Conserved Quantities by Interpreting Siamese Neural Networks S. J. Wetzel R. Melko Joseph Scott Maysum Panju Vijay Ganesh 45 66 0 09 Mar 2020
Discovering conservation laws from data for control E. Kaiser J. Nathan Kutz Steven L. Brunton 48 49 0 02 Nov 2018
Interpolating between Optimal Transport and MMD using Sinkhorn Divergences Jean Feydy Thibault Séjourné François-Xavier Vialard S. Amari A. Trouvé Gabriel Peyré OT 53 524 0 18 Oct 2018
Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance Jonathan Niles-Weed Francis R. Bach 130 417 0 01 Jul 2017
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration Jason M. Altschuler Jonathan Niles-Weed Philippe Rigollet OT 56 587 0 26 May 2017
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances Marco Cuturi OT 142 4,210 0 04 Jun 2013