Computer Science > Machine Learning
arXiv:2306.06034 (cs)
[Submitted on 9 Jun 2023 (v1), last revised 11 Aug 2023 (this version, v3)]
Title:RANS-PINN based Simulation Surrogates for Predicting Turbulent Flows
View a PDF of the paper titled RANS-PINN based Simulation Surrogates for Predicting Turbulent Flows, by Shinjan Ghosh and 3 other authors
View PDFAbstract:Physics-informed neural networks (PINNs) provide a framework to build surrogate models for dynamical systems governed by differential equations. During the learning process, PINNs incorporate a physics-based regularization term within the loss function to enhance generalization performance. Since simulating dynamics controlled by partial differential equations (PDEs) can be computationally expensive, PINNs have gained popularity in learning parametric surrogates for fluid flow problems governed by Navier-Stokes equations. In this work, we introduce RANS-PINN, a modified PINN framework, to predict flow fields (i.e., velocity and pressure) in high Reynolds number turbulent flow regimes. To account for the additional complexity introduced by turbulence, RANS-PINN employs a 2-equation eddy viscosity model based on a Reynolds-averaged Navier-Stokes (RANS) formulation. Furthermore, we adopt a novel training approach that ensures effective initialization and balance among the various components of the loss function. The effectiveness of the RANS-PINN framework is then demonstrated using a parametric PINN.
| Subjects: | Machine Learning (cs.LG); Numerical Analysis (math.NA); Fluid Dynamics (physics.flu-dyn) |
| Cite as: | arXiv:2306.06034 [cs.LG] |
| (orarXiv:2306.06034v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2306.06034 arXiv-issued DOI via DataCite | |
| Journal reference: | Published at the 1st workshop on Synergy of Scientific and Machine Learning Modeling, ICML 2023 |
Submission history
From: Biswadip Dey [view email][v1] Fri, 9 Jun 2023 16:55:49 UTC (2,968 KB)
[v2] Thu, 22 Jun 2023 16:03:04 UTC (2,968 KB)
[v3] Fri, 11 Aug 2023 16:17:54 UTC (2,876 KB)
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View a PDF of the paper titled RANS-PINN based Simulation Surrogates for Predicting Turbulent Flows, by Shinjan Ghosh and 3 other authors
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