Sample-Efficient and Surrogate-Based Design Optimization of Underwater Vehicle Hulls

arXiv:2304.12420v2 Announce Type: replace
Abstract: Physics simulations like computational fluid dynamics (CFD) are a computational bottleneck in computer-aided design (CAD) optimization processes. To overcome this bottleneck, one requires either an optimization framework that is highly sample-efficient, or a fast data-driven proxy (surrogate model) for long-running simulations. Both approaches have benefits and limitations. Bayesian optimization is often used for sample efficiency, but it solves one specific problem and struggles with transferability; alternatively, surrogate models can offer fast and often more generalizable …

abstract arxiv benefits cad cfd computational computer cs.ai cs.lg data data-driven design dynamics fluid dynamics framework optimization physics physics.app-ph physics.flu-dyn processes running sample simulations stat.ap stat.ml type underwater