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Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations
April 25, 2024, 7:43 p.m. | Sawan Kumar, Rajdip Nayek, Souvik Chakraborty
cs.LG updates on arXiv.org arxiv.org
Abstract: The study of neural operators has paved the way for the development of efficient approaches for solving partial differential equations (PDEs) compared with traditional methods. However, most of the existing neural operators lack the capability to provide uncertainty measures for their predictions, a crucial aspect, especially in data-driven scenarios with limited available data. In this work, we propose a novel Neural Operator-induced Gaussian Process (NOGaP), which exploits the probabilistic characteristics of Gaussian Processes (GPs) while …
abstract arxiv capability cs.lg development differential framework however operators parametric process solution stat.ml study the way type uncertainty
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