PINNs without initial conditions recover verifiable three-body periodic orbits from sparse noisy data, with training data—not init distribution—controlling which families emerge across seed ensembles.
Physics-informed neural networks without loss balancing: A direct term scaling approach for nonlinear 1D problems
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Physics-Informed Neural Networks for Discovering Periodic Orbits in the Gravitational Three-Body Problem
PINNs without initial conditions recover verifiable three-body periodic orbits from sparse noisy data, with training data—not init distribution—controlling which families emerge across seed ensembles.