Lagrangian Neural Networks

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13 papers6 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Dissipative Lagrangian Neural Networks for the Interpretable Modelling of Passive Mechanical Systems
Oct 11, 2026Rob Seabourne, Alice CicirelloLagrangian Neural Networks System Identification
Department of Engineering, University of Cambridge Trumpington Street, Cambridge CB2 1PZ, UK
Recovering Irreducible Three-Body Interactions with Lagrangian Neural Networks
Oct 9, 2026Sena Kalabalik, Sukru Caglar, Abdullah Umut Hamzaogullari +1Lagrangian Neural Networks Machine Learning Interatomic Potentials
Department of Physics, Bo˘gazi¸ci University, Istanbul, Türkiye · Department of Computer Engineering, Bo˘gazi¸ci University, Istanbul, Türkiye
Learning Schwarzschild Geodesic Motion with Lagrangian Neural Networks
Oct 9, 2026Sukru Caglar, Sena Kalabalik, Abdullah Umut Hamzaogullari +1Lagrangian Neural Networks
Department of Physics, Bogazici University, Istanbul, Turkey · Department of Computer Engineering, Bogazici University, Istanbul, Turkey
Latent-Lagrangian Neural Networks for Reduced Order Modeling of Non-autonomous Nonlinear Dynamical Systems
Oct 3, 2026Anand Kumar Agrawal, Anders ThorinLagrangian Neural Networks Reduced-Order Modeling
Université Paris-Saclay, CEA List, Palaiseau, France
Lagrangian and Hamiltonian Neural Networks With a Dissipative System
Sep 25, 2026V. Rayamajhi, J. SingalLagrangian Neural Networks Hamiltonian Neural Networks
University of Richmond
ExoLaN: Physics-Consistent Context-Aware Dynamics Learning for Exoskeletons
Sep 25, 2026Lucas Schulze, Maximilian Schwarz, Jona Hoppe +2Lagrangian Neural Networks Robot Dynamics Learning
Department of Computer Science, Technical University of Darmstadt, Germany · Robotics Institute Germany (RIG) · German Research Center for AI (DFKI), Research Department: Systems AI for Robot Learning
History-informed Lagrangian Neural Networks
Aug 13, 2026Tianshuo Zhang, Xianglei Xing, Wenzhe Zhai +2Latent Dynamics Modeling Physics-Informed ML
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements
Jun 30, 2026Kundan Kumar, Shreya Das, Simo SärkkäBayesian Filtering Nonlinear System Identification
K. Kumar is with the School of Engineering and Applied Science, Ahmedabad University, India · K. Kumar and S. Särkkä are with the ELLIS Institute Finland and the Department of Electrical Engineering and Automation, Aalto University, Finland · S. Das is with the Department of Electrical Engineering, Indian Institute of Technology (BHU) Varanasi, India
Learned Lagrangian Models of PDEs via Euler-Lagrange Residual Minimization
May 8, 2026Lyra Zhornyak, Eric Forgoston, M. Ani HsiehNeural PDE Solvers PDE Surrogate Modeling
University of Pennsylvania · Montclair State University
Data-Driven Physics Embedded Dynamics with Predictive Control and Reinforcement Learning for Quadrupeds
Mar 15, 2026Prakrut Kotecha, Aditya Shirwatkar, Shishir KolathayaLagrangian Neural Networks RL for Legged Locomotion
the Robert Bosch Center for Cyber-Physical Systems, Indian Institute of Science, Bengaluru. · the Robert Bosch Center for Cyber-Physical Systems and the Department of Computer Science & Automation, Indian Institute of Science, Bengaluru.
STRIDE: Structured Lagrangian and Stochastic Residual Dynamics via Flow Matching
Mar 9, 2026Prakrut Kotecha, Ganga Nair B, Shishir KolathayaLagrangian Neural Networks Robot Dynamics Learning
the Robert Bosch Center for Cyber-Physical Systems, Indian Institute of Science, Bengaluru · the Robert Bosch Center for Cyber-Physical Systems and the Department of Computer Science & Automation, Indian Institute of Science, Bengaluru
Integrating Lagrangian Neural Networks into the Dyna Framework for Reinforcement Learning
Mar 9, 2026Shreya Das, Kundan Kumar, Muhammad Iqbal +4Lagrangian Neural Networks Model-Based RL
Department of Electrical Engineering and Automation, Aalto University, Finland · VTT Technical Research Centre of Finland · Department of Computer Science, University of Helsinki, Finland +1
Deepmechanics
Feb 20, 2026Abhay Shinde, Aryan Amit Barsainyan, Jose Siguenza +3Symplectic RNNs Lagrangian Neural Networks
Deep Forest Sciences, Mumbai, Maharashtra, India · Deep Forest Sciences, Bengaluru, Karnataka, India · Deep Forest Sciences, Palo alto, California, USA +1

Oct 11, 2026J/K move · Enter open · S save

Dissipative Lagrangian Neural Networks for the Interpretable Modelling of Passive Mechanical Systems

Rob Seabourne, Alice Cicirello

Department of Engineering, University of Cambridge Trumpington Street, Cambridge CB2 1PZ, UK

Lagrangian Neural Networks System Identification Nonlinear System Identification Robot Dynamics Identification

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In this paper, we propose the Dissipative Lagrangian Neural Network (dLNN), a continuous-time system identification technique that separately learns the conservative and dissipative dynamics of a mechanical system directly from data. A key contribution is a novel parametrisation of dissipative forces that (i) is structurally constrained to be dissipative, enforcing passivity of the learned model, (ii) cannot absorb conservative mechanics, and (iii) retains sufficient expressivity to represent a broad family of state-dependent damping and regularised friction forces. The model is validated on prediction tasks on two real-world mechanical systems: trajectory prediction of a chaotic double pendulum and inverse-dynamics modelling of a six-degree-of-freedom industrial robot. Across these tasks, the dLNN demonstrates improved predictive performance over existing Lagrangian architectures while remaining competitive with a black-box neural model. The imposed physical structure also improves generalisation to unseen states, with the benefits becoming particularly pronounced when training data is limited. Finally, we introduce a symbolic distillation procedure that extracts closed-form expressions from the learned physical components. Applied to the industrial robot, this produces interpretable symbolic models that retain high predictive accuracy while reducing the computational overhead associated with evaluating neural Lagrangian models.