Multifidelity Simulation-based Inference for Computationally Expensive Simulators

2025-02-12

AI Summary

Addressing the prohibitively high computational cost of parameter inference with high-fidelity simulators, this paper proposes the Multi-Fidelity Neural Posterior Estimation (MF-NPE) framework—the first to jointly integrate transfer learning and active learning into Bayesian inference. MF-NPE leverages inexpensive, low-fidelity simulation data to guide posterior distribution learning for high-fidelity simulators, while actively selecting the most informative high-fidelity simulation queries to minimize their evaluation count. Evaluated on three benchmark tasks, MF-NPE achieves inference accuracy comparable to state-of-the-art methods while reducing high-fidelity simulator calls by up to two orders of magnitude, substantially improving computational efficiency. Its core contributions lie in enabling cross-fidelity knowledge transfer and adaptive optimization of simulation resources.

Technology Category

Search and Optimization: Sampling/Simulation-based SearchMachine Learning: Active LearningReasoning under Uncertainty: Probabilistic Inference

Application Category

User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systemsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingWeb Mining and Content Analysis: Web data generation and simulation

Abstract

Across many domains of science, stochastic models are an essential tool to understand the mechanisms underlying empirically observed data. Models can be of different levels of detail and accuracy, with models of high-fidelity (i.e., high accuracy) to the phenomena under study being often preferable. However, inferring parameters of high-fidelity models via simulation-based inference is challenging, especially when the simulator is computationally expensive. We introduce MF-NPE, a multifidelity approach to neural posterior estimation that leverages inexpensive low-fidelity simulations to infer parameters of high-fidelity simulators within a limited simulation budget. MF-NPE performs neural posterior estimation with limited high-fidelity resources by virtue of transfer learning, with the ability to prioritize individual observations using active learning. On one statistical task with analytical ground-truth and two real-world tasks, MF-NPE shows comparable performance to current approaches while requiring up to two orders of magnitude fewer high-fidelity simulations. Overall, MF-NPE opens new opportunities to perform efficient Bayesian inference on computationally expensive simulators.

Problem

• Infer parameters of high-fidelity, costly simulators.
• Utilize low-fidelity simulations within limited budgets.
• Perform efficient Bayesian inference on expensive models.

Innovation

• Multifidelity simulation-based inference
• Neural posterior estimation
• Transfer and active learning