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[2602.18060] Deepmechanics
Summary
The paper 'Deepmechanics' benchmarks physics-informed deep learning models for dynamical systems, revealing challenges in stability for chaotic and non-conservative systems.
Why It Matters
This research highlights the limitations of current physics-informed deep learning architectures in accurately modeling complex dynamical systems. As these models are increasingly applied in scientific contexts, understanding their performance and stability is crucial for advancing the field of machine learning in physics.
Key Takeaways
- Benchmarking of three physics-informed architectures: HNN, LNN, and SRNN.
- Models struggle with stability in chaotic and non-conservative systems.
- Need for further research to enhance robustness in classical mechanics modeling.
Computer Science > Machine Learning arXiv:2602.18060 (cs) [Submitted on 20 Feb 2026] Title:Deepmechanics Authors:Abhay Shinde, Aryan Amit Barsainyan, Jose Siguenza, Ankita Vaishnobi Bisoi, Rakshit Kr. Singh, Bharath Ramsundar View a PDF of the paper titled Deepmechanics, by Abhay Shinde and 5 other authors View PDF HTML (experimental) Abstract:Physics-informed deep learning models have emerged as powerful tools for learning dynamical systems. These models directly encode physical principles into network architectures. However, systematic benchmarking of these approaches across diverse physical phenomena remains limited, particularly in conservative and dissipative systems. In addition, benchmarking that has been done thus far does not integrate out full trajectories to check stability. In this work, we benchmark three prominent physics-informed architectures such as Hamiltonian Neural Networks (HNN), Lagrangian Neural Networks (LNN), and Symplectic Recurrent Neural Networks (SRNN) using the DeepChem framework, an open-source scientific machine learning library. We evaluate these models on six dynamical systems spanning classical conservative mechanics (mass-spring system, simple pendulum, double pendulum, and three-body problem, spring-pendulum) and non-conservative systems with contact (bouncing ball). We evaluate models by computing error on predicted trajectories and evaluate error both quantitatively and qualitatively. We find that all benchmarked models struggle to ma...
