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Found 6 papers, 2 papers with code
Reliability Analysis of Complex Systems using Subset Simulations with Hamiltonian Neural Networks
We present a new Subset Simulation approach using Hamiltonian neural network-based Monte Carlo sampling for reliability analysis.
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.
Efficient Interdependent Systems Recovery Modeling with DeepONets
Modeling the recovery of interdependent critical infrastructure is a key component of quantifying and optimizing societal resilience to disruptive events.
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.
