Pseudo-Hamiltonian neural networks for learning partial differential equations

27 April 2023

Papers citing "Pseudo-Hamiltonian neural networks for learning partial differential equations"

11 / 11 papers shown

Title
Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs Yusuke Tanaka Takaharu Yaguchi Tomoharu Iwata N. Ueda AI4CE 42 0 0 14 Feb 2024
Data-Driven Discovery of PDEs via the Adjoint Method Mohsen Sadr Tony Tohme Kamal Youcef-Toumi PINN 27 1 0 30 Jan 2024
Hierarchical deep learning-based adaptive time-stepping scheme for multiscale simulations Asif Hamid Danish Rafiq S. A. Nahvi M. A. Bazaz AI4CE 39 1 0 10 Nov 2023
Pseudo-Hamiltonian system identification Sigurd Holmsen Sølve Eidnes S. Riemer-Sørensen 18 3 0 09 May 2023
Predictions Based on Pixel Data: Insights from PDEs and Finite Differences E. Celledoni James Jackaman Davide Murari B. Owren 33 1 0 01 May 2023
Learning Hamiltonians of constrained mechanical systems E. Celledoni A. Leone Davide Murari B. Owren AI4CE 44 17 0 31 Jan 2022
Physics-informed neural networks with hard constraints for inverse design Lu Lu R. Pestourie Wenjie Yao Zhicheng Wang F. Verdugo Steven G. Johnson PINN 39 494 0 09 Feb 2021
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 220 2,287 0 18 Oct 2020
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems J. Willard X. Jia Shaoming Xu M. Steinbach Vipin Kumar AI4CE 91 387 0 10 Mar 2020
Lagrangian Neural Networks M. Cranmer S. Greydanus Stephan Hoyer Peter W. Battaglia D. Spergel S. Ho PINN 133 424 0 10 Mar 2020
Symplectic Recurrent Neural Networks Zhengdao Chen Jianyu Zhang Martín Arjovsky Léon Bottou 149 220 0 29 Sep 2019