MCMC Informed Neural Emulators for Uncertainty Quantification in Dynamical Systems
This provides a more efficient method for uncertainty quantification in surrogate models for dynamical systems, though it is incremental as it builds on existing neural network and MCMC techniques.
The paper tackles the problem of inefficient uncertainty quantification in neural network surrogates for dynamical systems by decoupling it from network architecture and using MCMC to incorporate model-parameter distributions as inputs, achieving the same uncertainty quantification as physical models with substantially reduced computation time.
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, the surrogate achieves the same uncertainty quantification as the underlying physical model, but with substantially reduced computation time. The approach is fully agnostic with respect to the neural network choice. In our examples, we present a quantile emulator for prediction and a novel autoencoder-based ODE network emulator that can flexibly estimate different trajectory paths corresponding to different ODE model parameters. Moreover, we present a mathematical analysis that provides a transparent way to relate potential performance loss to measurable distribution mismatch.