Surrogate Modeling of Landau Damping with Deep Operator Networks
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915478350331904 |
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| author | Shekarpaz, Simin Dong, Chuanfei Huang, Ziyu |
| author_facet | Shekarpaz, Simin Dong, Chuanfei Huang, Ziyu |
| contents | Kinetic simulations excel at capturing microscale plasma physics phenomena with high accuracy, but their computational demands make them impractical for modeling large-scale space and astrophysical systems. In this context, we build a surrogate model, using Deep Operator Networks (DeepONets), based upon the Vlasov-Poisson simulation data to model the dynamical evolution of plasmas, focusing on the Landau damping process - a fundamental kinetic phenomenon in space and astrophysical plasmas. The trained DeepONets are able to capture the evolution of electric field energy in both linear and nonlinear regimes under various conditions. Extensive validation highlights DeepONets' robust performance in reproducing complex plasma behaviors with high accuracy, paving the way for large-scale modeling of space and astrophysical plasmas. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_16960 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Surrogate Modeling of Landau Damping with Deep Operator Networks Shekarpaz, Simin Dong, Chuanfei Huang, Ziyu Plasma Physics High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics Computational Physics Space Physics Kinetic simulations excel at capturing microscale plasma physics phenomena with high accuracy, but their computational demands make them impractical for modeling large-scale space and astrophysical systems. In this context, we build a surrogate model, using Deep Operator Networks (DeepONets), based upon the Vlasov-Poisson simulation data to model the dynamical evolution of plasmas, focusing on the Landau damping process - a fundamental kinetic phenomenon in space and astrophysical plasmas. The trained DeepONets are able to capture the evolution of electric field energy in both linear and nonlinear regimes under various conditions. Extensive validation highlights DeepONets' robust performance in reproducing complex plasma behaviors with high accuracy, paving the way for large-scale modeling of space and astrophysical plasmas. |
| title | Surrogate Modeling of Landau Damping with Deep Operator Networks |
| topic | Plasma Physics High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics Computational Physics Space Physics |
| url | https://arxiv.org/abs/2507.16960 |