AI-Accelerated Operator Learning Framework for Rarefied Microflows
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Accesso online: | |
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| _version_ | 1866911324034826240 |
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| author | Roohi, Ehsan |
| author_facet | Roohi, Ehsan |
| contents | The high computational cost of kinetic solvers such as DSMC remains a major challenge in rarefied flow simulations. This work presents a unified framework combining deep neural networks and neural operators to accelerate kinetic and hybrid solvers while preserving physical fidelity. GPU-native DNN surrogates eliminate costly moment-closure operations in Fokker Planck methods, achieving significant speedups without accuracy loss, while physics-guided and shock-aware DeepONet architectures enable accurate, data efficient modeling of multi regime micro nozzle, micro-step, and hypersonic flows. Extensions including ensemble uncertainty quantification and family-of-experts strategies further enhance robustness across wide Mach and Knudsen number ranges. Together, these results demonstrate a scalable and physics-consistent pathway toward real-time surrogate modeling in rarefied gas dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15085 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Accelerated Operator Learning Framework for Rarefied Microflows Roohi, Ehsan Fluid Dynamics The high computational cost of kinetic solvers such as DSMC remains a major challenge in rarefied flow simulations. This work presents a unified framework combining deep neural networks and neural operators to accelerate kinetic and hybrid solvers while preserving physical fidelity. GPU-native DNN surrogates eliminate costly moment-closure operations in Fokker Planck methods, achieving significant speedups without accuracy loss, while physics-guided and shock-aware DeepONet architectures enable accurate, data efficient modeling of multi regime micro nozzle, micro-step, and hypersonic flows. Extensions including ensemble uncertainty quantification and family-of-experts strategies further enhance robustness across wide Mach and Knudsen number ranges. Together, these results demonstrate a scalable and physics-consistent pathway toward real-time surrogate modeling in rarefied gas dynamics. |
| title | AI-Accelerated Operator Learning Framework for Rarefied Microflows |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2512.15085 |