{"ID":2849230,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.24631","arxiv_id":"2510.24631","title":"Bridging Simulators with Conditional Optimal Transport","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.","short_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 ensur...","url_abs":"https://arxiv.org/abs/2510.24631","url_pdf":"https://arxiv.org/pdf/2510.24631v1","authors":"[\"Justine Zeghal\",\"Benjamin Remy\",\"Yashar Hezaveh\",\"Francois Lanusse\",\"Laurence Perreault Levasseur\"]","published":"2025-10-28T16:59:42Z","proceeding":"astro-ph.CO","tasks":"[\"astro-ph.CO\",\"stat.ML\"]","methods":"[]","has_code":false}