Hamiltonian Neural Networks

4 June 2019

Papers citing "Hamiltonian Neural Networks"

50 / 185 papers shown

Title
Modular Robot Control with Motor Primitives Moses C. Nah Johannes Lachner Neville Hogan 19 0 0 15 May 2025
Geometric Fault-Tolerant Neural Network Tracking Control of Unknown Systems on Matrix Lie Groups Robin Chhabra Farzaneh Abdollahi 36 0 0 07 May 2025
Hamiltonian Normalizing Flows as kinetic PDE solvers: application to the 1D Vlasov-Poisson Equations Vincent Souveton Sébastien Terrana 41 0 0 07 May 2025
Learning and Transferring Physical Models through Derivatives Alessandro Trenta Andrea Cossu Davide Bacciu AI4CE 39 0 0 02 May 2025
Provably-Safe, Online System Identification Bohao Zhang Zichang Zhou Ram Vasudevan 48 0 0 30 Apr 2025
Negative Imaginary Neural ODEs: Learning to Control Mechanical Systems with Stability Guarantees Kanghong Shi Ruigang Wang I. Manchester 24 0 0 28 Apr 2025
New Recipe for Semi-supervised Community Detection: Clique Annealing under Crystallization Kinetics Ling Cheng Jiashu Pu Ruicheng Liang Qian Shao Hezhe Qiao Feida Zhu 31 0 0 22 Apr 2025
High-order expansion of Neural Ordinary Differential Equations flows Dario Izzo Sebastien Origer Giacomo Acciarini F. Biscani AI4CE 29 0 0 02 Apr 2025
Gradient Networks Shreyas Chaudhari Srinivasa Pranav J. M. F. Moura 58 0 0 28 Jan 2025
Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery Yana Lishkova P. Scherer Steffen Ridderbusch M. Jamnik Pietro Lio Sina Ober-Blobaum Christian Offen PINN 73 7 0 28 Jan 2025
AnyNav: Visual Neuro-Symbolic Friction Learning for Off-road Navigation Taimeng Fu Zitong Zhan Zhipeng Zhao Shaoshu Su Xiao Lin Ehsan Esfahani Karthik Dantu Souma Chowdhury Chen Wang 90 1 0 22 Jan 2025
Optimal Virtual Model Control for Robotics: Design and Tuning of Passivity-Based Controllers Daniel Larby F. Forni 62 1 0 20 Jan 2025
Understanding and Mitigating Membership Inference Risks of Neural Ordinary Differential Equations Sanghyun Hong Fan Wu A. Gruber Kookjin Lee 42 0 0 12 Jan 2025
Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Cyrus Neary Nathan Tsao Ufuk Topcu 77 1 0 15 Dec 2024
Explicit and data-Efficient Encoding via Gradient Flow Kyriakos Flouris Anna Volokitin G. Bredell E. Konukoglu AI4CE 74 0 0 01 Dec 2024
A Riemannian Framework for Learning Reduced-order Lagrangian Dynamics Katharina Friedl Noémie Jaquier Jens Lundell Tamim Asfour Danica Kragic AI4CE 28 0 0 24 Oct 2024
Efficient, Accurate and Stable Gradients for Neural ODEs Sam McCallum James Foster 37 4 0 15 Oct 2024
Feedback Favors the Generalization of Neural ODEs Jindou Jia Zihan Yang Meng Wang Kexin Guo Jianfei Yang Xiang Yu Lei Guo OOD AI4CE 43 2 0 14 Oct 2024
Unsupervised Representation Learning from Sparse Transformation Analysis Yue Song Thomas Anderson Keller Yisong Yue Pietro Perona Max Welling DRL 31 0 0 07 Oct 2024
SymmetryLens: A new candidate paradigm for unsupervised symmetry learning via locality and equivariance Onur Efe Arkadas Ozakin 31 0 0 07 Oct 2024
Scientific Machine Learning Seismology Tomohisa Okazaki PINN AI4CE 53 0 0 27 Sep 2024
Analysis of the Identifying Regulation with Adversarial Surrogates Algorithm Ron Teichner Ron Meir Michael Margaliot 25 0 0 05 May 2024
Implicit-Explicit simulation of Mass-Spring-Charge Systems Zhiyuan Zhang Zhaocheng Liu Stefanos‐Aldo Papanicolopulos Kartic Subr Kartic Subr PINN 37 0 0 05 Mar 2024
Closure Discovery for Coarse-Grained Partial Differential Equations Using Grid-based Reinforcement Learning Jan-Philipp von Bassewitz Sebastian Kaltenbach Petros Koumoutsakos AI4CE 38 2 0 01 Feb 2024
Bayesian identification of nonseparable Hamiltonians with multiplicative noise using deep learning and reduced-order modeling Nicholas Galioto Harsh Sharma Boris Kramer Alex Arkady Gorodetsky 44 0 0 23 Jan 2024
Port-Hamiltonian Neural ODE Networks on Lie Groups For Robot Dynamics Learning and Control T. Duong Abdullah Altawaitan Jason Stanley Nikolay Atanasov 28 10 0 17 Jan 2024
Hybrid Modeling Design Patterns Maja Rudolph Stefan Kurz Barbara Rakitsch AI4CE 31 8 0 29 Dec 2023
A charge-preserving method for solving graph neural diffusion networks Lidia Aceto Pietro Antonio Grassi 32 0 0 16 Dec 2023
Neural Autoencoder-Based Structure-Preserving Model Order Reduction and Control Design for High-Dimensional Physical Systems Marco Lepri Davide Bacciu Cosimo Della Santina AI4CE 38 8 0 11 Dec 2023
A hybrid approach for solving the gravitational N-body problem with Artificial Neural Networks V. S. Ulibarrena Philipp Horn S. P. Zwart E. Sellentin B. Koren Maxwell X. Cai PINN 22 2 0 31 Oct 2023
Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach Kai Zhao Qiyu Kang Yang Song Rui She Sijie Wang Wee Peng Tay AAML 40 22 0 10 Oct 2023
Training neural mapping schemes for satellite altimetry with simulation data Q. Febvre Julien Le Sommer C. Ubelmann Ronan Fablet 11 9 0 19 Sep 2023
Learning Hybrid Dynamics Models With Simulator-Informed Latent States K. Ensinger Sebastian Ziesche Sebastian Trimpe 34 1 0 06 Sep 2023
Hamiltonian GAN Christine Allen-Blanchette GAN AI4CE 40 1 0 22 Aug 2023
Predicting and explaining nonlinear material response using deep Physically Guided Neural Networks with Internal Variables Javier Orera-Echeverria J. Ayensa-Jiménez Manuel Doblaré 25 1 0 07 Aug 2023
Iterative Magnitude Pruning as a Renormalisation Group: A Study in The Context of The Lottery Ticket Hypothesis Abu-Al Hassan 33 0 0 06 Aug 2023
Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks S. Bishnoi Ravinder Bhattoo J. Jayadeva Sayan Ranu N. M. A. Krishnan PINN AI4CE 34 1 0 11 Jul 2023
Learning Latent Dynamics via Invariant Decomposition and (Spatio-)Temporal Transformers Kai Lagemann C. Lagemann Swarnava Mukherjee 39 2 0 21 Jun 2023
Learning Dynamical Systems from Noisy Data with Inverse-Explicit Integrators Haakon Noren Sølve Eidnes E. Celledoni 21 3 0 06 Jun 2023
Physics-Informed Computer Vision: A Review and Perspectives C. Banerjee Kien Nguyen Clinton Fookes G. Karniadakis PINN AI4CE 34 29 0 29 May 2023
Learning Switching Port-Hamiltonian Systems with Uncertainty Quantification Thomas Beckers Tom Z. Jiahao George J. Pappas 31 2 0 15 May 2023
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior Thomas Beckers Jacob H. Seidman P. Perdikaris George J. Pappas PINN 29 17 0 15 May 2023
Policy Gradient Methods in the Presence of Symmetries and State Abstractions Prakash Panangaden S. Rezaei-Shoshtari Rosie Zhao D. Meger Doina Precup 25 2 0 09 May 2023
Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models Sarvin Moradi N. Jaensson Roland Tóth Maarten Schoukens PINN 30 3 0 02 May 2023
Pseudo-Hamiltonian neural networks for learning partial differential equations Sølve Eidnes K. Lye 26 10 0 27 Apr 2023
Constraining Chaos: Enforcing dynamical invariants in the training of recurrent neural networks Jason A. Platt S. Penny T. A. Smith Tse-Chun Chen H. Abarbanel AI4TS 36 5 0 24 Apr 2023
Contingency Analyses with Warm Starter using Probabilistic Graphical Model Shimiao Li Amritanshu Pandey L. Pileggi AI4CE 34 2 0 10 Apr 2023
Learning Hamiltonian Systems with Mono-Implicit Runge-Kutta Methods Haakon Noren 27 3 0 07 Mar 2023
MetaPhysiCa: OOD Robustness in Physics-informed Machine Learning S Chandra Mouli M. A. Alam Bruno Ribeiro OOD 29 4 0 06 Mar 2023
Node Embedding from Hamiltonian Information Propagation in Graph Neural Networks Qiyu Kang Kai Zhao Yang Song Sijie Wang Rui She Wee Peng Tay 38 0 0 02 Mar 2023