Automatic Inference for Inverting Software Simulators via Probabilistic Programming

TL;DR

This paper introduces a probabilistic programming framework that simplifies inverting complex software simulators, enabling automatic inference with minimal code, demonstrated on a geological simulator.

Contribution

It presents a novel, concise probabilistic programming approach for inverting complex simulators, reducing implementation effort and broadening applicability.

Findings

Successfully inverted a real geological simulator

Implemented four inference strategies with minimal code

Demonstrated effectiveness on industry-relevant models

Abstract

Models of complex systems are often formalized as sequential software simulators: computationally intensive programs that iteratively build up probable system configurations given parameters and initial conditions. These simulators enable modelers to capture effects that are difficult to characterize analytically or summarize statistically. However, in many real-world applications, these simulations need to be inverted to match the observed data. This typically requires the custom design, derivation and implementation of sophisticated inversion algorithms. Here we give a framework for inverting a broad class of complex software simulators via probabilistic programming and automatic inference, using under 20 lines of probabilistic code. Our approach is based on a formulation of inversion as approximate inference in a simple sequential probabilistic model. We implement four inference strategies, including Metropolis-Hastings, a sequentialized Metropolis-Hastings scheme, and a particle Markov chain Monte Carlo scheme, requiring 4 or fewer lines of probabilistic code each. We demonstrate our framework by applying it to invert a real geological software simulator from the oil and gas industry.