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20162023
most citedMulti-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities

130 citations · 327 across the 10 of their papers we have counts for

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2 papers · 1 filter

cs.LG2023★ 1 cited

PDE-constrained Gaussian process surrogate modeling with uncertain data locations

Dongwei Ye, Weihao Yan, Christoph Brune +1

Gaussian process regression is widely applied in computational science and engineering for surrogate modeling owning to its kernel-based and probabilistic nature. In this work, we…

math.NA2023★ 2 cited

Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression

Ludovica Cicci, Stefania Fresca, Mengwu Guo +2

Uncertainty quantification (UQ) tasks, such as sensitivity analysis and parameter estimation, entail a huge computational complexity when dealing with input-output maps involving t…