Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2512.18595 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917240436162560 |
|---|---|
| author | Mao, Runze Zhang, Rui Bai, Xuan Wu, Tianhao Zhang, Teng Chen, Zhenyi Lin, Minqi Zeng, Bocheng Xu, Yangchen Xiang, Yingxuan Zhang, Haoze Goswami, Shubham Dawe, Pierre A. Xu, Yifan An, Zhenhua Yan, Mengtao Lu, Xiaoyi Wang, Yi Bai, Rongbo Gao, Haobu Fang, Xiaohang Li, Han Sun, Hao Chen, Zhi X. |
| author_facet | Mao, Runze Zhang, Rui Bai, Xuan Wu, Tianhao Zhang, Teng Chen, Zhenyi Lin, Minqi Zeng, Bocheng Xu, Yangchen Xiang, Yingxuan Zhang, Haoze Goswami, Shubham Dawe, Pierre A. Xu, Yifan An, Zhenhua Yan, Mengtao Lu, Xiaoyi Wang, Yi Bai, Rongbo Gao, Haobu Fang, Xiaohang Li, Han Sun, Hao Chen, Zhi X. |
| contents | Predicting multiphysics dynamics is computationally expensive and challenging due to the severe coupling of multi-scale, heterogeneous physical processes. While neural surrogates promise a paradigm shift, the field currently suffers from an "illusion of mastery", as repeatedly emphasized in top-tier commentaries: existing evaluations overly rely on simplified, low-dimensional proxies, which fail to expose the models' inherent fragility in realistic regimes. To bridge this critical gap, we present REALM (REalistic AI Learning for Multiphysics), a rigorous benchmarking framework designed to test neural surrogates on challenging, application-driven reactive flows. REALM features 11 high-fidelity datasets spanning from canonical multiphysics problems to complex propulsion and fire safety scenarios, alongside a standardized end-to-end training and evaluation protocol that incorporates multiphysics-aware preprocessing and a robust rollout strategy. Using this framework, we systematically benchmark over a dozen representative surrogate model families, including spectral operators, convolutional models, Transformers, pointwise operators, and graph/mesh networks, and identify three robust trends: (i) a scaling barrier governed jointly by dimensionality, stiffness, and mesh irregularity, leading to rapidly growing rollout errors; (ii) performance primarily controlled by architectural inductive biases rather than parameter count; and (iii) a persistent gap between nominal accuracy metrics and physically trustworthy behavior, where models with high correlations still miss key transient structures and integral quantities. Taken together, REALM exposes the limits of current neural surrogates on realistic multiphysics flows and offers a rigorous testbed to drive the development of next-generation physics-aware architectures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_18595 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Benchmarking neural surrogates on realistic spatiotemporal multiphysics flows Mao, Runze Zhang, Rui Bai, Xuan Wu, Tianhao Zhang, Teng Chen, Zhenyi Lin, Minqi Zeng, Bocheng Xu, Yangchen Xiang, Yingxuan Zhang, Haoze Goswami, Shubham Dawe, Pierre A. Xu, Yifan An, Zhenhua Yan, Mengtao Lu, Xiaoyi Wang, Yi Bai, Rongbo Gao, Haobu Fang, Xiaohang Li, Han Sun, Hao Chen, Zhi X. Machine Learning Predicting multiphysics dynamics is computationally expensive and challenging due to the severe coupling of multi-scale, heterogeneous physical processes. While neural surrogates promise a paradigm shift, the field currently suffers from an "illusion of mastery", as repeatedly emphasized in top-tier commentaries: existing evaluations overly rely on simplified, low-dimensional proxies, which fail to expose the models' inherent fragility in realistic regimes. To bridge this critical gap, we present REALM (REalistic AI Learning for Multiphysics), a rigorous benchmarking framework designed to test neural surrogates on challenging, application-driven reactive flows. REALM features 11 high-fidelity datasets spanning from canonical multiphysics problems to complex propulsion and fire safety scenarios, alongside a standardized end-to-end training and evaluation protocol that incorporates multiphysics-aware preprocessing and a robust rollout strategy. Using this framework, we systematically benchmark over a dozen representative surrogate model families, including spectral operators, convolutional models, Transformers, pointwise operators, and graph/mesh networks, and identify three robust trends: (i) a scaling barrier governed jointly by dimensionality, stiffness, and mesh irregularity, leading to rapidly growing rollout errors; (ii) performance primarily controlled by architectural inductive biases rather than parameter count; and (iii) a persistent gap between nominal accuracy metrics and physically trustworthy behavior, where models with high correlations still miss key transient structures and integral quantities. Taken together, REALM exposes the limits of current neural surrogates on realistic multiphysics flows and offers a rigorous testbed to drive the development of next-generation physics-aware architectures. |
| title | Benchmarking neural surrogates on realistic spatiotemporal multiphysics flows |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.18595 |