2 papers
cs.LG2026
Goal-Driven Adaptive Sampling Strategies for Machine Learning Models Predicting Fields
Jigar Parekh, Philipp Bekemeyer
Machine learning models are widely regarded as a way forward to tackle multi-query challenges that arise once expensive black-box simulations such as computational fluid dynamics a…
cs.CE2024
Partitioned Surrogates and Thompson Sampling for Multidisciplinary Bayesian Optimization
Susanna Baars, Jigar Parekh, Ihar Antonau +2
The long runtime associated with simulating multidisciplinary systems challenges the use of Bayesian optimization for multidisciplinary design optimization (MDO). This is particula…