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GECCO2021 - wksp119 - WS - SAEOpt - Black-box Mixed-Variable Optimisation Using a Surrogate [...]
GPTune: Non-smooth and Mixed Variables in Surrogate Models -- Hengrui Luo
Surrogate Modeling in Engineering: Designing Under Uncertainty with AI | Uplatz
Surrogate Modeling and Active Learning for Optimization | Fireside Chat with Dr. Bobby Gramacy
339 - Surrogate Optimization explained using simple python code
Regression Models (Surrogate Based Opt.)
AMPL Tutorial 3: Training Regression Models for Cheminformatics with AMPL
The Surrogate Solution Bypassing the Computational Bottleneck
Module 3: Neural Networks & Surrogate Models
Surrogate Modeling: Enhancing Analysis and Optimization through Efficient Approximations
Infill (Surrogate Based Opt.)
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Last Updated: October 14, 2026
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Summary
Speaker: Meinolf Sellmann, senior manager of Cognitive Data Curation at IBM Research Our objective is to tune Speaker: Juli Mueller U.S. National Renewable Energy Laboratory Summary: Computationally expensive black-box optimization ... Hengrui Luo presents "GPTune: Non-smooth and Mixed Variables in Engineering systems are increasingly complex, and traditional simulation methods can be computationally expensive and slow. Thought Leader: Dr. Bobby Gramacy is a Professor of Statistics at Virginia Tech and a Fellow of the American Statistical ... In Tutorial 3 of the AMPL video series, you'll learn how to train machine learning Infill, exploitation and exploration, basic
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