Flow Matching for Posterior Inference with Simulator Feedback
Keywords: generative modeling, simulation-based inference, astronomy
TL;DR: We show that feedback from simulators gives a greatly improved posterior accuracy when solving inverse problems with flow matching
Abstract: Flow-based generative modeling is a powerful tool for solving inverse problems in physical sciences that can be used for sampling and likelihood evaluation with much lower inference times than traditional methods. We propose to refine flows with additional control signals based on a simulator. Control signals can include gradients and a problem-specific cost function if the simulator is differentiable, or they can be fully learned from the simulator output.
In our proposed method, we pretrain the flow network and include feedback from the simulator exclusively for finetuning, therefore requiring only a small amount of additional parameters and compute. We motivate our design choices on several benchmark problems for simulation-based inference and evaluate flow matching with simulator feedback against classical MCMC methods for modeling strong gravitational lens systems, a challenging inverse problem in astronomy. We demonstrate that including feedback from the simulator improves the accuracy by $53$%, making it competitive with traditional techniques while being up to 67x faster for inference. Upon acceptance, we will make our code publicly available.
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 1338
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