Bridging Simulators with Conditional Optimal Transport
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911237022941184 |
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| author | Zeghal, Justine Remy, Benjamin Hezaveh, Yashar Lanusse, Francois Levasseur, Laurence Perreault |
| author_facet | Zeghal, Justine Remy, Benjamin Hezaveh, Yashar Lanusse, Francois Levasseur, Laurence Perreault |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24631 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridging Simulators with Conditional Optimal Transport Zeghal, Justine Remy, Benjamin Hezaveh, Yashar Lanusse, Francois Levasseur, Laurence Perreault Cosmology and Nongalactic Astrophysics Machine Learning 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. |
| title | Bridging Simulators with Conditional Optimal Transport |
| topic | Cosmology and Nongalactic Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2510.24631 |