Dissipative Lagrangian Neural Networks for the Interpretable Modelling of Passive Mechanical Systems
cs.LG, cs.CE
Submitted: 2026-10-11
Updated: 2026-10-11
Code: https://github.com/robseabourne/Dissipative_Lagrangian_Neural_
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- Physics-Enhanced Machine Learning: a position paper for dynamical systems investigations
- Universal Differential Equations for Scientific Machine Learning
- Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately
- Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics
- Learning Relativistic Geodesics and Chaotic Dynamics via Stabilized Lagrangian Neural Networks
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks