By Yian Chen and Mihai Anitescu

Physics-based covariance models provide a systematic way to construct covariance models that are consistent with the underlying physical laws in Gaussian process analysis. The unknown parameters in the covariance models can be estimated using maximum likelihood estimation, but direct construction of the covariance matrix and classical strategies of computing with... Show more

March 17, 2023

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Scalable Physics-based Maximum Likelihood Estimation using Hierarchical Matrices

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