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Non-Stationary Kernel Learning in Gaussian Processes
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Abstract. Gaussian processes (GPs) are powerful probabilistic models that define flexible priors over functions, offering strong interpretability and uncertainty quantification. However, GP models often rely on simple, ...
Data-Driven Calibration of Multifidelity Multiscale Fracture Models Via Latent Map Gaussian Process
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Fracture modeling of metallic alloys with microscopic pores relies on multiscale damage simulations which typically ignore the manufacturing-induced spatial variabilities in porosity. This simplification is made because ...
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