Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.10919 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917261721206784 |
|---|---|
| author | Menicali, Luca Grace, Andrew Richter, David H. Castruccio, Stefano |
| author_facet | Menicali, Luca Grace, Andrew Richter, David H. Castruccio, Stefano |
| contents | Fluid thermodynamics underpins atmospheric dynamics, climate science, industrial applications, and energy systems. However, direct numerical simulations (DNS) of such systems can be computationally prohibitive. To address this, we present a novel physics-informed spatiotemporal surrogate model for Rayleigh-Benard convection (RBC), a canonical example of convective fluid flow. Our approach combines convolutional neural networks, for spatial dimension reduction, with an innovative recurrent architecture, inspired by large language models, to model long-range temporal dynamics. Inference is penalized with respect to the governing partial differential equations to ensure physical interpretability. Since RBC exhibits turbulent behavior, we quantify uncertainty using a conformal prediction framework. This model replicates key physical features of RBC dynamics while significantly reducing computational cost, offering a scalable alternative to DNS for long-term simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10919 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems Menicali, Luca Grace, Andrew Richter, David H. Castruccio, Stefano Fluid Dynamics Machine Learning Fluid thermodynamics underpins atmospheric dynamics, climate science, industrial applications, and energy systems. However, direct numerical simulations (DNS) of such systems can be computationally prohibitive. To address this, we present a novel physics-informed spatiotemporal surrogate model for Rayleigh-Benard convection (RBC), a canonical example of convective fluid flow. Our approach combines convolutional neural networks, for spatial dimension reduction, with an innovative recurrent architecture, inspired by large language models, to model long-range temporal dynamics. Inference is penalized with respect to the governing partial differential equations to ensure physical interpretability. Since RBC exhibits turbulent behavior, we quantify uncertainty using a conformal prediction framework. This model replicates key physical features of RBC dynamics while significantly reducing computational cost, offering a scalable alternative to DNS for long-term simulations. |
| title | A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems |
| topic | Fluid Dynamics Machine Learning |
| url | https://arxiv.org/abs/2505.10919 |