Blending physics with data using an efficient Gaussian process regression with soft inequality and monotonicity constraints
In this work, we propose a new Gaussian process (GP) regression framework that enforces the physical constraints in a probabilistic manner. Specifically, we focus on inequality and monotonicity constraints. This GP model is trained by the quantum-inspired Hamiltonian Monte Carlo (QHMC) algorithm, wh...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Mechanical Engineering |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmech.2024.1410190/full |
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