Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately

25 January 2022

Papers citing "Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately"

21 / 21 papers shown

Title
Symplectic Neural Flows for Modeling and Discovery Priscilla Canizares Davide Murari Carola-Bibiane Schönlieb Ferdia Sherry Zakhar Shumaylov 80 1 0 21 Dec 2024
Training Hamiltonian neural networks without backpropagation Atamert Rahma Chinmay Datar Felix Dietrich 62 0 0 26 Nov 2024
Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics? Tae-Geun Kim Seong Chan Park 23 0 0 28 Oct 2024
Learning dissipative Hamiltonian dynamics with reproducing kernel Hilbert spaces and random Fourier features Torbjørn Smith Olav Egeland 21 0 0 24 Oct 2024
Lagrangian Neural Networks for Reversible Dissipative Evolution V. Sundararaghavan Megna N. Shah Jeff P. Simmons PINN 30 0 0 23 May 2024
A comparison of Single- and Double-generator formalisms for Thermodynamics-Informed Neural Networks Pau Urdeitx Ic´ıar Alfaro David González Francisco Chinesta Elías Cueto AI4CE 30 1 0 01 Apr 2024
Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs Yusuke Tanaka Takaharu Yaguchi Tomoharu Iwata N. Ueda AI4CE 37 0 0 14 Feb 2024
Structure-Preserving Physics-Informed Neural Networks With Energy or Lyapunov Structure Haoyu Chu Yuto Miyatake Wenjun Cui Shikui Wei Daisuke Furihata PINN 15 1 0 10 Jan 2024
Discussing the Spectrum of Physics-Enhanced Machine Learning; a Survey on Structural Mechanics Applications M. Haywood-Alexander Wei Liu Kiran Bacsa Zhilu Lai Eleni Chatzi AI4CE 13 9 0 31 Oct 2023
Symmetry Preservation in Hamiltonian Systems: Simulation and Learning M. Vaquero Jorge Cortés David Martín de Diego 15 4 0 30 Aug 2023
Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models Sarvin Moradi N. Jaensson Roland Tóth Maarten Schoukens PINN 27 3 0 02 May 2023
Pseudo-Hamiltonian neural networks for learning partial differential equations Sølve Eidnes K. Lye 18 10 0 27 Apr 2023
Gaussian processes at the Helm(holtz): A more fluid model for ocean currents Renato Berlinghieri Brian L. Trippe David R. Burt Ryan Giordano K. Srinivasan Tamay Ozgokmen Junfei Xia Tamara Broderick 13 10 0 20 Feb 2023
Data-driven discovery of non-Newtonian astronomy via learning non-Euclidean Hamiltonian Oswin So Gongjie Li Evangelos A. Theodorou Molei Tao AI4CE 15 3 0 30 Sep 2022
Constants of motion network M. F. Kasim Yi Heng Lim 10 4 0 22 Aug 2022
Unifying physical systems' inductive biases in neural ODE using dynamics constraints Yi Heng Lim M. F. Kasim PINN AI4CE 12 5 0 03 Aug 2022
KeyCLD: Learning Constrained Lagrangian Dynamics in Keypoint Coordinates from Images Rembert Daems Jeroen Taets Francis Wyffels Guillaume Crevecoeur 11 1 0 22 Jun 2022
Neural Implicit Representations for Physical Parameter Inference from a Single Video Florian Hofherr Lukas Koestler Florian Bernard Daniel Cremers AI4CE 34 9 0 29 Apr 2022
Learning Neural Hamiltonian Dynamics: A Methodological Overview Zhijie Chen Mingquan Feng Junchi Yan H. Zha AI4CE 19 15 0 28 Feb 2022
Dissipative Deep Neural Dynamical Systems Ján Drgoňa Soumya Vasisht Aaron Tuor D. Vrabie 19 6 0 26 Nov 2020
Symplectic Recurrent Neural Networks Zhengdao Chen Jianyu Zhang Martín Arjovsky Léon Bottou 143 219 0 29 Sep 2019