Aug 1, 2023 · In this study, we propose a hybrid data driven-physics constrained Gaussian process regression framework. We use GPR with a deep kernel as the ...
May 13, 2022 · In this work, we propose a hybrid data driven-physics constrained Gaussian process regression framework. We encode the physics knowledge with ...
Aug 1, 2023 · In this study, we propose a hybrid data-driven-physics-constrained Gaussian process regression framework. We encode the physics knowledge with a ...
In this study, we propose a hybrid data-driven-physics-constrained Gaussian process regression framework. We encode the physics knowledge with a Boltzmann-Gibbs ...
Nov 10, 2022 · In this work, we propose a hybrid data driven-physics constrained Gaussian process regression framework. We use the GPR with deep kernel as the ...
In this work, we propose a hybrid data driven-physics constrained Gaussian process regression framework. We encode the physics knowledge with Boltzmann-Gibbs ...
We develop an efficient, Bayesian Uncertainty Quantification framework using a novel treed Gaussian process model. The tree is adaptively constructed using ...
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May 17, 2022 · Bibliographic details on A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty ...
A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification. Cheng Chang, Tieyong Zeng. Nov ...
May 22, 2024 · A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification · Scalable ...