Uncertainty in AI-driven Monte Carlo simulations

TL;DR

This paper introduces the Penalty Ensemble Method (PEM), a technique to quantify and mitigate epistemic uncertainty in AI-accelerated Monte Carlo simulations, improving their reliability in complex system modeling.

Contribution

The paper proposes PEM, an uncertainty-aware modification to the Metropolis algorithm, to better handle model uncertainty in AI-driven Monte Carlo methods.

Findings

PEM effectively quantifies epistemic uncertainty.

PEM improves the accuracy of Monte Carlo simulations.

The modified acceptance rule reduces errors in uncertain regions.

Abstract

In the study of complex systems, evaluating physical observables often requires sampling representative configurations via Monte Carlo techniques. These methods rely on repeated evaluations of the system's energy and force fields, which can become computationally expensive. To accelerate these simulations, deep learning models are increasingly employed as surrogate functions to approximate the energy landscape or force fields. However, such models introduce epistemic uncertainty in their predictions, which may propagate through the sampling process and affect the system's macroscopic behavior. In our work, we present the Penalty Ensemble Method (PEM) to quantify epistemic uncertainty and mitigate its impact on Monte Carlo sampling. Our approach introduces an uncertainty-aware modification of the Metropolis acceptance rule, which increases the rejection probability in regions of high uncertainty, thereby enhancing the reliability of the simulation outcomes.