Multi-fidelity surrogate with heterogeneous input spaces for modeling melt pools in laser-directed energy deposition

arXiv:2403.13136v1 Announce Type: new
Abstract: Multi-fidelity (MF) modeling is a powerful statistical approach that can intelligently blend data from varied fidelity sources. This approach finds a compelling application in predicting melt pool geometry for laser-directed energy deposition (L-DED). One major challenge in using MF surrogates to merge a hierarchy of melt pool models is the variability in input spaces. To address this challenge, this paper introduces a novel approach for constructing an MF surrogate for predicting melt pool geometry by …

abstract application arxiv blend challenge cs.lg cs.na data energy fidelity geometry major math.na melt modeling pool spaces statistical type