MCMC Informed Neural Emulators for Uncertainty Quantification in Dynamical Systems
Neural networks are a commonly used approach to replace physical models with computationally cheap surrogates. Parametric uncertainty quantification can be included in training, assuming that an accurate prior distribution of the model parameters is available. Here we study the common opposite situation, where direct screening or random sampling of model parameters leads to exhaustive training times and evaluations at unphysical parameter values. Our solution is to decouple uncertainty quantification from network architecture. Instead of sampling network weights, we introduce the model-parameter distribution as an input to network training via Markov chain Monte Carlo (MCMC). In this way,...
| Authors | Heikki Haario, Zhi-Song Liu, Martin Simon et al. |
|---|---|
| Primary category | cs.LG |
| Published | 11 March 2026 |
| Last updated at source | 11 March 2026 |
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