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Found 3 papers, 1 papers with code
Multi-fidelity Design of Porous Microstructures for Thermofluidic Applications
We address these challenges by developing a data-driven framework for designing optimal porous microstructures for cooling applications.
Probabilistic Neural Data Fusion for Learning from an Arbitrary Number of Multi-fidelity Data Sets
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.
Data Fusion with Latent Map Gaussian Processes
Multi-fidelity modeling and calibration are data fusion tasks that ubiquitously arise in engineering design.
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