معرفی
Pedro Diez is a researcher specializing in computational methods for geotechnical and geophysical applications. His work focuses on developing data-driven models, particularly Reduced-Order Models (ROM) and Bayesian solvers for inverse problems. He explores advanced techniques like kernel Principal Component Analysis (kPCA) combined with Proper Orthogonal Decomposition (POD) to optimize dimensionality reduction in parametric forward problems. His research emphasizes uncertainty quantification using Markov-Chain Monte Carlo (MCMC) strategies, addressing challenges in geophysical crust dynamics and large-scale parameter identification.
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