Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification

arXiv:2402.15115v1 Announce Type: cross
Abstract: We present a novel physics-constrained polynomial chaos expansion as a surrogate modeling method capable of performing both scientific machine learning (SciML) and uncertainty quantification (UQ) tasks. The proposed method possesses a unique capability: it seamlessly integrates SciML into UQ and vice versa, which allows it to quantify the uncertainties in SciML tasks effectively and leverage SciML for improved uncertainty assessment during UQ-related tasks. The proposed surrogate model can effectively incorporate a variety of physical constraints, …

abstract arxiv capability chaos cs.lg expansion machine machine learning modeling novel physics physics.data-an polynomial quantification stat.ml tasks type uncertainty