Probabilistic Neural Data Fusion for Learning from an Arbitrary Number of Multi-fidelity Data Sets

1 code implementation • 30 Jan 2023 • Carlos Mora, Jonathan Tammer Eweis-Labolle, Tyler Johnson, Likith Gadde, Ramin Bostanabad

Our NN architecture inversely learns non-trivial (e. g., non-additive and non-hierarchical) biases of the LF sources in an interpretable and visualizable manifold where each data source is encoded via a low-dimensional distribution.