Probabilistic Surrogate Networks for Simulators with Unbounded Randomness

TL;DR

This paper introduces a novel deep neural surrogate modeling framework for stochastic simulators with potentially unbounded random variables, enabling faster simulation and inference while maintaining interpretability.

Contribution

It presents a method for automatically constructing interpretable neural surrogates that handle unbounded stochastic randomness, improving simulation speed and inference accuracy.

Findings

Surrogates accurately model complex probabilistic programs with unbounded variables.

The approach significantly accelerates amortized inference in black-box simulators.

Demonstrated effectiveness in materials testing and program synthesis examples.

Abstract

We present a framework for automatically structuring and training fast, approximate, deep neural surrogates of stochastic simulators. Unlike traditional approaches to surrogate modeling, our surrogates retain the interpretable structure and control flow of the reference simulator. Our surrogates target stochastic simulators where the number of random variables itself can be stochastic and potentially unbounded. Our framework further enables an automatic replacement of the reference simulator with the surrogate when undertaking amortized inference. The fidelity and speed of our surrogates allow for both faster stochastic simulation and accurate and substantially faster posterior inference. Using an illustrative yet non-trivial example we show our surrogates' ability to accurately model a probabilistic program with an unbounded number of random variables. We then proceed with an example that shows our surrogates are able to accurately model a complex structure like an unbounded stack in a program synthesis example. We further demonstrate how our surrogate modeling technique makes amortized inference in complex black-box simulators an order of magnitude faster. Specifically, we do simulator-based materials quality testing, inferring safety-critical latent internal temperature profiles of composite materials undergoing curing.