@misc{0cb90ee12b1d48e29b6b303abbd4f076,
title = "Adaptive Computing and Multi-Fidelity Learning",
abstract = "We describe our ongoing research in adaptive computing. Our goal is to use a combination of low- and high-fidelity simulation models to enable computationally efficient optimization and uncertainty quantification. We develop optimization formulations that take into account the compute resources currently available, which act as a constraint with regards to the fidelity level simulation we can run while maximizing information gain. We will discuss a few application examples that can benefit from this approach, especially when considering challenges arising in scaling up experiments and simulations.",
keywords = "black-box optimization, multi-fidelity optimization, surrogate model",
author = "Juliane Mueller and Marc Day and Hilary Egan and Ryan King and Kevin Griffin and Jibo Sanyal",
year = "2023",
doi = "10.2172/3014930",
language = "American English",
series = "Presented at IPAM Workshop III: Complex Scientific Workflows at Extreme Computational Scales, 1-5 May 2023, Los Angeles, California",
type = "Other",
}