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Found 3 papers, 2 papers with code
Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling
Multi-fidelity surrogate modeling aims to learn an accurate surrogate at the highest fidelity level by combining data from multiple sources.
Disentangled Multi-Fidelity Deep Bayesian Active Learning
To balance quality and cost, various domain areas of science and engineering run simulations at multiple levels of sophistication.
Deep Bayesian Active Learning for Accelerating Stochastic Simulation
We propose Interactive Neural Process (INP), a deep Bayesian active learning framework for learning deep surrogate models to accelerate stochastic simulations.
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