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Lagrangian Neural Networks Learn Complex Three-Body Interactions

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Lagrangian Neural Networks (LNNs) capable of learning complex mechanical systems from trajectory data. These LNNs are designed to respect the variational structure of mechanics by parameterizing a scalar Lagrangian and deriving dynamics through Euler-Lagrange equations. In a novel application, the LNNs successfully recovered the irreducible Axilrod-Teller triple-dipole interaction, a three-body force, from motion data alone. This was achieved by learning only the potential energy function, which demonstrated accurate reproduction of the interaction's radial scaling, angular dependence, and sign reversal, even when embedded within a pairwise potential. AI

IMPACT Demonstrates potential for AI to discover fundamental physical laws from observational data.

RANK_REASON Academic paper detailing a new method for learning physical interactions using neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Lagrangian Neural Networks Learn Complex Three-Body Interactions

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Academic paper detailing a new method for learning physical interactions using neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Sena Kalabalik, Sukru Caglar, Abdullah Umut Hamzaogullari, Arkadas Ozakin ·

    Recovering Irreducible Three-Body Interactions with Lagrangian Neural Networks

    arXiv:2610.13292v1 Announce Type: new Abstract: Lagrangian Neural Networks (LNNs) learn mechanical systems from trajectory data by parameterizing a scalar Lagrangian and deriving the dynamics through the Euler-Lagrange equations, biasing the model toward motion that respects the …

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