PDE-DKL: PDE-constrained deep kernel learning in high dimensionality
Many physics-informed machine learning methods for PDE-based problems rely on Gaussian processes (GPs) or neural networks (NNs). However, both face limitations when data are scarce and the dimensionality is high. Although GPs are known for their robust uncertainty quantification in low-dimensional s...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
30.01.2025
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Subjects | |
Online Access | Get full text |
DOI | 10.48550/arxiv.2501.18258 |
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