ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918520770527232 |
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| author | Li, Chengze Wei, Lingwei Sun, Li Lv, Hongbo Yang, Jie Zhang, Hanrong Zheng, Kening Huang, Wei-Chieh Ma, Enze Yu, Philip S. |
| author_facet | Li, Chengze Wei, Lingwei Sun, Li Lv, Hongbo Yang, Jie Zhang, Hanrong Zheng, Kening Huang, Wei-Chieh Ma, Enze Yu, Philip S. |
| contents | Partial differential equation (PDE) foundation models are pretrained networks that forecast how physical fields like velocity and pressure evolve from a single reusable solver. On unfamiliar flows their predictions drift step by step, errors concentrate in a few regions, yet retraining destabilizes the network and uniform post-hoc correction overlooks this spatial concentration. To address this, we propose a frozen-solver post-hoc correction framework, Adaptive Risk-Calibrated Spatial Triage for Auditable Refinement (ARC-STAR). ARC-STAR organizes correction into three stages: a global corrector removes broad solver bias, a blockwise local refiner cleans the post-global residual, and, at deployment, a label-free score routes refinement to high-risk blocks under a compute budget. The framework is designed to be (i) frozen-host, preserving the pretrained solver without fine-tuning; (ii) auditable, with global and local stages trained and evaluated separately for measurable contributions; and (iii) budget-aware, using a blockwise interface that either refines the full field or routes limited compute to high-risk regions. Across five flow benchmarks spanning ten regime cells, ARC-STAR is the only method that cuts velocity rollout error by at least 36x over raw Poseidon on every cell. The global stage reduces raw host error by 91-99%, and the local stage further reduces the remaining post-global residual by up to 94.4%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_22222 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models Li, Chengze Wei, Lingwei Sun, Li Lv, Hongbo Yang, Jie Zhang, Hanrong Zheng, Kening Huang, Wei-Chieh Ma, Enze Yu, Philip S. Machine Learning Partial differential equation (PDE) foundation models are pretrained networks that forecast how physical fields like velocity and pressure evolve from a single reusable solver. On unfamiliar flows their predictions drift step by step, errors concentrate in a few regions, yet retraining destabilizes the network and uniform post-hoc correction overlooks this spatial concentration. To address this, we propose a frozen-solver post-hoc correction framework, Adaptive Risk-Calibrated Spatial Triage for Auditable Refinement (ARC-STAR). ARC-STAR organizes correction into three stages: a global corrector removes broad solver bias, a blockwise local refiner cleans the post-global residual, and, at deployment, a label-free score routes refinement to high-risk blocks under a compute budget. The framework is designed to be (i) frozen-host, preserving the pretrained solver without fine-tuning; (ii) auditable, with global and local stages trained and evaluated separately for measurable contributions; and (iii) budget-aware, using a blockwise interface that either refines the full field or routes limited compute to high-risk regions. Across five flow benchmarks spanning ten regime cells, ARC-STAR is the only method that cuts velocity rollout error by at least 36x over raw Poseidon on every cell. The global stage reduces raw host error by 91-99%, and the local stage further reduces the remaining post-global residual by up to 94.4%. |
| title | ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.22222 |