Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

10 July 2019

Jan Peters

Papers citing "Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning"

50 / 211 papers shown

Title
Learning continuous models for continuous physics Aditi S. Krishnapriyan A. Queiruga N. Benjamin Erichson Michael W. Mahoney AI4CE 21 32 0 17 Feb 2022
Deconstructing the Inductive Biases of Hamiltonian Neural Networks Nate Gruver Marc Finzi Samuel Stanton A. Wilson AI4CE 15 39 0 10 Feb 2022
Spectrally Adapted Physics-Informed Neural Networks for Solving Unbounded Domain Problems Mingtao Xia Lucas Böttcher T. Chou 16 19 0 06 Feb 2022
Knowledge-Integrated Informed AI for National Security Anu Myne Kevin J. Leahy Ryan Soklaski 19 0 0 04 Feb 2022
Learning Physics-Consistent Particle Interactions Zhichao Han David S. Kammer Olga Fink 11 7 0 01 Feb 2022
Approximately Equivariant Networks for Imperfectly Symmetric Dynamics Rui Wang Robin G. Walters Rose Yu 32 73 0 28 Jan 2022
Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately A. Sosanya S. Greydanus PINN AI4CE 38 27 0 25 Jan 2022
Symplectic Momentum Neural Networks -- Using Discrete Variational Mechanics as a prior in Deep Learning Saul Santos Monica Ekal R. Ventura 23 5 0 20 Jan 2022
Control of Dual-Sourcing Inventory Systems using Recurrent Neural Networks Lucas Böttcher Thomas Asikis I. Fragkos BDL 16 10 0 16 Jan 2022
Constraint-based graph network simulator Yulia Rubanova Alvaro Sanchez-Gonzalez Tobias Pfaff Peter W. Battaglia PINN AI4CE 27 28 0 16 Dec 2021
Noether Networks: Meta-Learning Useful Conserved Quantities Ferran Alet Dylan D. Doblar Allan Zhou J. Tenenbaum Kenji Kawaguchi Chelsea Finn 67 26 0 06 Dec 2021
Physically Consistent Neural Networks for building thermal modeling: theory and analysis L. D. Natale B. Svetozarevic Philipp Heer Colin N. Jones PINN AI4CE 57 84 0 06 Dec 2021
Learning Large-Time-Step Molecular Dynamics with Graph Neural Networks Tian Zheng Weihao Gao Chong-Jun Wang AI4CE 23 3 0 30 Nov 2021
Physics-enhanced Neural Networks in the Small Data Regime Jonas Eichelsdörfer Sebastian Kaltenbach P. Koutsourelakis AI4CE PINN 9 5 0 19 Nov 2021
Physics-informed neural networks via stochastic Hamiltonian dynamics learning Chandrajit L. Bajaj Minh Nguyen 13 1 0 15 Nov 2021
Climate Modeling with Neural Diffusion Equations JeeHyun Hwang Jeongwhan Choi Hwan-Kyu Choi Kookjin Lee Dongeun Lee Noseong Park DiffM 19 22 0 11 Nov 2021
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision I. Higgins Peter Wirnsberger Andrew Jaegle Aleksandar Botev 37 7 0 10 Nov 2021
Which priors matter? Benchmarking models for learning latent dynamics Aleksandar Botev Andrew Jaegle Peter Wirnsberger Daniel Hennes I. Higgins AI4CE 22 28 0 09 Nov 2021
Constructing Neural Network-Based Models for Simulating Dynamical Systems Christian Møldrup Legaard Thomas Schranz G. Schweiger Ján Drgovna Basak Falay C. Gomes Alexandros Iosifidis M. Abkar P. Larsen PINN AI4CE 28 93 0 02 Nov 2021
Robot Learning from Randomized Simulations: A Review Fabio Muratore Fabio Ramos Greg Turk Wenhao Yu Michael Gienger Jan Peters AI4CE 10 79 0 01 Nov 2021
Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC Felix Bünning B. Huber Adrian Schalbetter Ahmed Aboudonia Mathias Hudoba de Badyn Philipp Heer Roy S. Smith John Lygeros AI4CE 14 65 0 29 Oct 2021
A Differentiable Newton-Euler Algorithm for Real-World Robotics M. Lutter Vallijah Subasri Joe Watson Frank Rudzicz 22 7 0 24 Oct 2021
Extracting Dynamical Models from Data M. Zimmer 20 1 0 13 Oct 2021
Lagrangian Neural Network with Differentiable Symmetries and Relational Inductive Bias Ravinder Bhattoo Sayan Ranu N. M. A. Krishnan AI4CE 47 4 0 07 Oct 2021
Continuous-Time Fitted Value Iteration for Robust Policies M. Lutter Boris Belousov Shie Mannor D. Fox Animesh Garg Jan Peters 8 9 0 05 Oct 2021
Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models M. Lutter Jan Peters PINN AI4CE 33 39 0 05 Oct 2021
OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation J. Wong Viktor Makoviychuk Anima Anandkumar Yuke Zhu 29 11 0 02 Oct 2021
Neural Network Verification in Control M. Everett AAML 32 16 0 30 Sep 2021
Learning Dynamics Models for Model Predictive Agents M. Lutter Leonard Hasenclever Arunkumar Byravan Gabriel Dulac-Arnold Piotr Trochim N. Heess J. Merel Yuval Tassa AI4CE 57 26 0 29 Sep 2021
Lyapunov-stable neural-network control Hongkai Dai Benoit Landry Lujie Yang Marco Pavone Russ Tedrake 10 118 0 29 Sep 2021
Modular Neural Ordinary Differential Equations Max Zhu P. Lio Jacob Moss PINN 32 2 0 15 Sep 2021
Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling Franck Djeumou Cyrus Neary Eric Goubault S. Putot Ufuk Topcu PINN AI4CE 39 68 0 14 Sep 2021
Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling Kookjin Lee Nathaniel Trask P. Stinis 32 24 0 11 Sep 2021
Plug and Play, Model-Based Reinforcement Learning Majid Abdolshah Hung Le T. G. Karimpanal Sunil R. Gupta Santu Rana Svetha Venkatesh OCL OffRL 11 0 0 20 Aug 2021
ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting Yu Huang James Li Min Shi H. Zhuang Xingquan Zhu Laurent Chérubin James H. VanZwieten Yufei Tang AI4CE PINN 23 6 0 12 Aug 2021
Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations Xing Chen Flavio Abreu Araujo M. Riou J. Torrejon D. Ravelosona W. Kang Weisheng Zhao Julie Grollier D. Querlioz 19 40 0 23 Jul 2021
Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems Shaan Desai M. Mattheakis David Sondak P. Protopapas Stephen J. Roberts AI4CE 25 43 0 16 Jul 2021
Machine learning structure preserving brackets for forecasting irreversible processes Kookjin Lee Nathaniel Trask P. Stinis AI4CE 33 42 0 23 Jun 2021
Controlling Neural Networks with Rule Representations Sungyong Seo Sercan Ö. Arik Jinsung Yoon Xiang Zhang Kihyuk Sohn Tomas Pfister OOD AI4CE 27 35 0 14 Jun 2021
Scalars are universal: Equivariant machine learning, structured like classical physics Soledad Villar D. Hogg Kate Storey-Fisher Weichi Yao Ben Blum-Smith PINN AI4CE 24 130 0 11 Jun 2021
Forced Variational Integrator Networks for Prediction and Control of Mechanical Systems Aaron J. Havens Girish Chowdhary PINN OOD AI4CE 11 8 0 05 Jun 2021
Machine-Learning Non-Conservative Dynamics for New-Physics Detection Ziming Liu Bohan Wang Qi Meng Wei Chen M. Tegmark Tie-Yan Liu PINN AI4CE 9 15 0 31 May 2021
SyReNets: Symbolic Residual Neural Networks Carlos Magno Catharino Olsson Valle Sami Haddadin 11 1 0 30 May 2021
Simulated Data Generation Through Algorithmic Force Coefficient Estimation for AI-Based Robotic Projectile Launch Modeling Sajiv Shah Ayaan Haque Fei Liu 6 0 0 09 May 2021
Training Structured Mechanical Models by Minimizing Discrete Euler-Lagrange Residual Kunal Menda Jayesh K. Gupta Zachary Manchester Mykel J. Kochenderfer 16 0 0 05 May 2021
Weak Form Generalized Hamiltonian Learning Kevin Course Trefor W. Evans P. Nair AI4CE 13 9 0 11 Apr 2021
GEM: Group Enhanced Model for Learning Dynamical Control Systems Philippe Hansen-Estruch Wenling Shang Lerrel Pinto Pieter Abbeel Stas Tiomkin AI4CE 25 2 0 07 Apr 2021
Learning Deep Energy Shaping Policies for Stability-Guaranteed Manipulation S. A. Khader Hang Yin Pietro Falco Danica Kragic 6 12 0 30 Mar 2021
Implicit energy regularization of neural ordinary-differential-equation control Lucas Böttcher Nino Antulov-Fantulin Thomas Asikis 21 66 0 11 Mar 2021
Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps Ren-Chuen Chen Molei Tao 24 49 0 09 Mar 2021