Projected Neural Differential Equations for Learning Constrained Dynamics

31 October 2024

Papers citing "Projected Neural Differential Equations for Learning Constrained Dynamics"

21 / 21 papers shown

Title
Machine Learning for Predicting Chaotic Systems Christof Schötz Alistair J R White Maximilian Gelbrecht Niklas Boers AI4CE 51 4 0 29 Jul 2024
Neural General Circulation Models for Weather and Climate Dmitrii Kochkov J. Yuval I. Langmore Peter C. Norgaard Jamie A. Smith ... Peter W. Battaglia Alvaro Sanchez-Gonzalez Matthew Willson Michael P. Brenner Stephan Hoyer AI4Cl AI4CE 115 154 0 13 Nov 2023
Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints Alistair J R White Niki Kilbertus Maximilian Gelbrecht Niklas Boers 48 7 0 16 Jun 2023
Neural Conservation Laws: A Divergence-Free Perspective Jack Richter-Powell Y. Lipman Ricky T. Q. Chen 103 55 0 04 Oct 2022
Differentiable Programming for Earth System Modeling Maximilian Gelbrecht Alistair J R White S. Bathiany Niklas Boers 57 18 0 29 Aug 2022
Physics-Informed Neural Operator for Learning Partial Differential Equations Zong-Yi Li Hongkai Zheng Nikola B. Kovachki David Jin Haoxuan Chen Burigede Liu Kamyar Azizzadenesheli Anima Anandkumar AI4CE 121 421 0 06 Nov 2021
Optimization on manifolds: A symplectic approach G. Francca Alessandro Barp Mark Girolami Michael I. Jordan 39 11 0 23 Jul 2021
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets Sizhuang He Hanwen Wang P. Perdikaris AI4CE 97 699 0 19 Mar 2021
Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems Spencer M. Richards Navid Azizan Jean-Jacques E. Slotine Marco Pavone 66 72 0 07 Mar 2021
Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints Marc Finzi Ke Alexander Wang A. Wilson AI4CE 70 129 0 26 Oct 2020
When and why PINNs fail to train: A neural tangent kernel perspective Sizhuang He Xinling Yu P. Perdikaris 141 914 0 28 Jul 2020
Riemannian Continuous Normalizing Flows Emile Mathieu Maximilian Nickel AI4CE 96 126 0 18 Jun 2020
Neural Manifold Ordinary Differential Equations Aaron Lou Derek Lim Isay Katsman Leo Huang Qingxuan Jiang Ser-Nam Lim Christopher De Sa BDL AI4CE 74 81 0 18 Jun 2020
Lagrangian Neural Networks M. Cranmer S. Greydanus Stephan Hoyer Peter W. Battaglia D. Spergel S. Ho PINN 173 437 0 10 Mar 2020
Normalizing Flows on Tori and Spheres Danilo Jimenez Rezende George Papamakarios S. Racanière M. S. Albergo G. Kanwar P. Shanahan Kyle Cranmer TPM 84 156 0 06 Feb 2020
Variational Optimization on Lie Groups, with Examples of Leading (Generalized) Eigenvalue Problems Molei Tao T. Ohsawa DRL 80 17 0 27 Jan 2020
Universal Differential Equations for Scientific Machine Learning Christopher Rackauckas Yingbo Ma Julius Martensen Collin Warner K. Zubov R. Supekar Dominic J. Skinner Ali Ramadhan Alan Edelman AI4CE 86 592 0 13 Jan 2020
Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control Yaofeng Desmond Zhong Biswadip Dey Amit Chakraborty PINN 88 271 0 26 Sep 2019
Neural Ordinary Differential Equations T. Chen Yulia Rubanova J. Bettencourt David Duvenaud AI4CE 417 5,111 0 19 Jun 2018
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations M. Raissi P. Perdikaris George Karniadakis PINN AI4CE 91 613 0 28 Nov 2017
Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations M. Raissi P. Perdikaris George Karniadakis PINN AI4CE 82 929 0 28 Nov 2017