A multi-physics digital twin method and system based on spatially selective Bayesian inference and a medium

Abstract

The application discloses a multi-physical field digital twin method and system based on spatial selection Bayesian inference and a medium, mainly relates to the technical field of multi-physical field digital twin, and is used to solve the problem that the existing scheme conflicts between calculation efficiency and physical fidelity. It includes: determining the physical probe range, obtaining each node in the physical probe range; based on the predicted temperature field, extracting the node predicted temperature of each node in the physical probe range, and then calculating the mean value as the final prediction value of the sensor; obtaining the actual observation value of each sensor; based on the actual observation value and the final prediction value, calculating the log-likelihood function value and the joint log-posterior probability of the current global operation proposal parameter value; using a multi-chain parallel MCMC algorithm, generating a new global operation proposal parameter value based on the current global operation proposal parameter value; based on the joint log-posterior probability, judging whether to accept the updated global operation proposal parameter value.

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