Learning Schwarzschild Geodesic Motion with Lagrangian Neural Networks
Lagrangian Neural Networks (LNNs) learn dynamics from trajectory data by parameterizing a scalar Lagrangian and deriving accelerations through the Euler--Lagrange equation, providing a variational inductive bias that improves physical consistency over purely data-driven models. LNNs have recently been extended to general relativistic settings, leaving Schwarzschild geodesics as an open challenge. Training on complex relativistic systems is hindered by the diversity of the dynamics, as well as numerical instabilities and the difficulty of adequately sampling physically relevant, diverse orbital regimes. We introduce two coordinated improvements: a decoupled position--velocity normalization...
| Authors | Sukru Caglar, Sena Kalabalik, Abdullah Umut Hamzaogullari et al. |
|---|---|
| Primary category | cs.LG |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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