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arXiv · 2510.24631

Bridging Simulators with Conditional Optimal Transport

Justine Zeghal·Benjamin Remy·Yashar Hezaveh·Francois Lanusse·Laurence Perreault Levasseur
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Abstract

We propose a new field-level emulator that bridges two simulators using unpaired simulation datasets. Our method leverages a flow-based approach to learn the likelihood transport from one simulator to the other. Since multiple transport maps exist, we employ Conditional Optimal Transport Flow Matching (COT-FM) to ensure that the transformation minimally distorts the underlying structure of the data. We demonstrate the effectiveness of this approach by bridging weak lensing simulators: a Lagrangian Perturbation Theory (LPT) to a N-body Particle-Mesh (PM). We demonstrate that our emulator captures the full correction between the simulators by showing that it enables full-field inference to accurately recover the true posterior, validating its accuracy beyond traditional summary statistics.

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Justine Zeghal, Benjamin Remy, Yashar Hezaveh, Francois Lanusse, Laurence Perreault Levasseur. 2025-10-28. Bridging Simulators with Conditional Optimal Transport. https://arxiv.org/abs/2510.24631

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