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• W4386287563 abstract "Electromagnetic particle-in-cell (EMPIC) algorithms have been a popular choice for simulating kinetic plasmas due to their ability to accurately capture complex transient nonlinear plasma phenomena. Despite the accuracy of EMPIC simulations, they incur high very high computational costs. This is a direct consequence of the large number of particles necessary to obtain accurate results. In this chapter, we discuss the use of Koopman autoencoders as effective machine learning based reduced-order models to mitigate the computational burden of traditional EMPIC algorithms. We discuss how deep learning architectures provide a natural data-driven route for approximating finite-dimensional Koopman invariant subspaces associated with kinetic plasma problems. We also provide a framework for the inclusion of physics-informed constraints into Koopman autoencoders models for this class of problems." @default.
• W4386287563 created "2023-08-31" @default.
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• W4386287563 date "2023-08-30" @default.
• W4386287563 modified "2023-10-16" @default.
• W4386287563 title "Koopman Autoencoders for Reduced‐Order Modeling of Kinetic Plasmas" @default.
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• W4386287563 doi "https://doi.org/10.1002/9781119853923.ch17" @default.
• W4386287563 hasPublicationYear "2023" @default.
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