Juliane Mueller - Adaptive Computing and multi-fidelity learning - IPAM at UCLA
Recorded 04 May 2023. Juliane Mueller of the National Renewable Energy Laboratory presents "Adaptive Computing and multi-fidelity learning" at IPAM's workshop for Complex Scientific Workflows at Extreme Computational Scales.
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.
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