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Surrogate Model Based Optimization and Active Learning for HPC Applications -- Juliane Mueller
339 - Surrogate Optimization explained using simple python code
Infill (Surrogate Based Opt.)
Surrogate Modeling and Active Learning for Optimization | Fireside Chat with Dr. Bobby Gramacy
Surrogate modeling and Bayesian optimization
Simon Weissmann - Surrogate based one-shot formulation for inverse problems and optimization
Regression Models (Surrogate Based Opt.)
Gaussian Process Based Surrogate Models
Surrogate models
Computationally Expensive Optimization: A Surrogate Model-informed Design and Analysis Approach
Surrogate modeling and Bayesian optimization (Part 2)
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Last Updated: October 14, 2026
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Summary
Simulation models are widely used in practice to facilitate decision-making in a complex, dynamic and stochastic environment. In this lecture for Stanford's AA 222 / CS 361 Engineering Design Speaker: Juli Mueller U.S. National Renewable Energy Laboratory Summary: Computationally expensive black-box Infill, exploitation and exploration, basic algorithm, expected improvement. Thought Leader: Dr. Bobby Gramacy is a Professor of Statistics at Virginia Tech and a Fellow of the American Statistical ... This talk was part of the Workshop on "PDE-constrained Bayesian inverse problems: interplay of spatial statistical models with ... Linear regression, least squares, nonlinear regression, cross validation, Gaussian process regression (e.g., Kriging) So the idea is to do a sequential I was invited by the IEEE Computational Intelligence Society, Australian Capital Territory Chapter, 09 September 2021 to deliver a ...
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