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arXiv Machine Learning By Sena Kalabalik, Sukru Caglar, Abdullah Umut Hamzaogullari, Arkadas Ozakin

Recovering Irreducible Three-Body Interactions with Lagrangian Neural Networks

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Lagrangian Neural Networks (LNNs) can learn irreducible three‑body interactions from trajectory data by parameterizing only the potential while prescribing kinetic energy. Using the Axilrod‑Teller triple‑dipole interaction as a test case, the authors show that a signal‑aware sampling strategy allows the LNN to recover the correct radial scaling, angular dependence, and sign reversal from accelerations alone. Symbolic regression on the trained potential reproduces the exact Axilrod‑Teller expression, demonstrating that LNNs can uncover closed‑form many‑body laws without prior functional assumptions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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