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.
Meyer, Glen R
1 Pith paper cite this work, alongside 317 external citations. Polarity classification is still indexing.
1
Pith paper citing it
317
external citations · OpenAlex
fields
cs.LG 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.