Scientific Machine Learning Based Reduced-Order Models for Plasma Turbulence Simulations
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arXiv
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| Format: | Preprint |
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2024
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| author | Gahr, Constantin Farcas, Ionut-Gabriel Jenko, Frank |
| author_facet | Gahr, Constantin Farcas, Ionut-Gabriel Jenko, Frank |
| contents | This paper investigates non-intrusive Scientific Machine Learning (SciML) Reduced-Order Models (ROMs) for plasma turbulence simulations. In particular, we focus on Operator Inference (OpInf) to build low-cost physics-based ROMs from data for such simulations. As a representative example, we consider the (classical) Hasegawa-Wakatani (HW) equations used for modeling two-dimensional electrostatic drift-wave turbulence. For a comprehensive perspective of the potential of OpInf to construct predictive ROMs, we consider three setups for the HW equations by varying a key parameter, namely the adiabaticity coefficient. These setups lead to the formation of complex and nonlinear dynamics, which makes the construction of predictive ROMs of any kind challenging. We generate the training datasets by performing direct numerical simulations of the HW equations and recording the computed state data and outputs the over a time horizon of $100$ time units in the turbulent phase. We then use these datasets to construct OpInf ROMs for predictions over $400$ additional time units, that is, $400\%$ more than the training horizon. Our results show that the OpInf ROMs capture important statistical features of the turbulent dynamics and generalize beyond the training time horizon while reducing the computational effort of the high-fidelity simulation by up to five orders of magnitude. In the broader context of fusion research, this shows that non-intrusive SciML ROMs have the potential to drastically accelerate numerical studies, which can ultimately enable tasks such as the design of optimized fusion devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05972 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scientific Machine Learning Based Reduced-Order Models for Plasma Turbulence Simulations Gahr, Constantin Farcas, Ionut-Gabriel Jenko, Frank Computational Physics Computational Engineering, Finance, and Science Machine Learning Plasma Physics This paper investigates non-intrusive Scientific Machine Learning (SciML) Reduced-Order Models (ROMs) for plasma turbulence simulations. In particular, we focus on Operator Inference (OpInf) to build low-cost physics-based ROMs from data for such simulations. As a representative example, we consider the (classical) Hasegawa-Wakatani (HW) equations used for modeling two-dimensional electrostatic drift-wave turbulence. For a comprehensive perspective of the potential of OpInf to construct predictive ROMs, we consider three setups for the HW equations by varying a key parameter, namely the adiabaticity coefficient. These setups lead to the formation of complex and nonlinear dynamics, which makes the construction of predictive ROMs of any kind challenging. We generate the training datasets by performing direct numerical simulations of the HW equations and recording the computed state data and outputs the over a time horizon of $100$ time units in the turbulent phase. We then use these datasets to construct OpInf ROMs for predictions over $400$ additional time units, that is, $400\%$ more than the training horizon. Our results show that the OpInf ROMs capture important statistical features of the turbulent dynamics and generalize beyond the training time horizon while reducing the computational effort of the high-fidelity simulation by up to five orders of magnitude. In the broader context of fusion research, this shows that non-intrusive SciML ROMs have the potential to drastically accelerate numerical studies, which can ultimately enable tasks such as the design of optimized fusion devices. |
| title | Scientific Machine Learning Based Reduced-Order Models for Plasma Turbulence Simulations |
| topic | Computational Physics Computational Engineering, Finance, and Science Machine Learning Plasma Physics |
| url | https://arxiv.org/abs/2401.05972 |