CORDIS Project
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This research combines data with physical knowledge using Bayesian inference and fluid dynamics. It aims to improve the accuracy and efficiency of models in various applications, including medical imaging and gas turbine analysis, by leveraging advanced computational techniques.
The proposed research will demonstrate, with industrial examples, how scientists and engineers can efficiently combine data with prior physical knowledge.
It will leverage Bayesian inference for rigour, adjoint methods for speed, and fluid dynamics for impact.
Bayesian inference has been applied to fluid dynamics before but rarely using adjoint-accelerated inference and optimization methods (AXIOMs).
These methods are crucial for practical applications because they dramatically accelerate data a…
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