Angeline Hu, Ava K. Tse, Kaitlyn S. Yasumura, Luciana Sarabia, Malcolm Yeung, Kavin K. Prasanna, Kai Wong, William Hammond · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28487 · open-access PDF
私たちは、動的に許容可能な極端なシナリオを発見するための2段階のデータ駆動のニューラルフレームワークを導入します。支配方程式の事前の知識を仮定するのではなく、最初の段階は、観測データから直接効果的なダイナミック制約を引き出します。基礎的な物理システムが適切なシンメトリーを持っているとき、学習したダイナミクスは、固定グリッド、座標システム、次元性、および内在の曲線に関係なく、ジオメトリーアグニチックな形で表現することができます。
Machine translation (Meta M2M100); the English original follows.
We introduce a two-stage data-driven neural framework for discovering dynamically admissible extreme scenarios. Rather than assuming prior knowledge of the governing equations, the first stage infers the effective dynamical constraints directly from observational data. When the underlying physical system possesses suitable symmetries, the learned dynamics can be represented in a geometry-agnostic form, independent of any fixed grid, coordinate system, dimensionality, and intrinsic curvature. In the second stage, a physics-informed neural-search uses this learned dynamics to uncover rare, high-
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