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Found 5 papers, 2 papers with code
General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation
The multi-fidelity surrogate is assembled by first applying a Gaussian process correction to each low-fidelity model and assigning a model probability based on the model's local predictive accuracy and cost.
Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference
We propose the use of HNNs for performing Bayesian inference efficiently without requiring numerous posterior gradients.
Bayesian Inference with Latent Hamiltonian Neural Networks
Compared to traditional NUTS, L-HNNs in NUTS with online error monitoring required 1--2 orders of magnitude fewer numerical gradients of the target density and improved the effective sample size (ESS) per gradient by an order of magnitude.
Reliability Estimation of an Advanced Nuclear Fuel using Coupled Active Learning, Multifidelity Modeling, and Subset Simulation
However, TRISO failure probabilities are small and the associated computational models are expensive.
Machine learning-assisted surrogate construction for full-core fuel performance analysis
Such one-way coupling is result of the high cost induced by the full-core fuel performance analysis, which provides more realistic and accurate prediction of the core-wide response than the "peak rod" analysis.
