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5 March 2021 Parameter space exploration of short-pulse laser-driven ion acceleration via ensemble simulations and neural networks
Blagoje Z. Djordjevic, Andreas Kemp, Joohwan Kim, Scott Wilks, Raspberry Simpson, Tammy Ma, Derek Mariscal
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Proceedings Volume 11666, High Power Lasers for Fusion Research VI; 116660E (2021) https://doi.org/10.1117/12.2587014
Event: SPIE LASE, 2021, Online Only
Abstract
Application of deep learning to shaped, short-pulse laser-driven ion acceleration. Using a neural network as a universal approximator function, i.e., a surrogate model, we can map out large areas of parameter space. The neural network is informed by a large dataset of about 1,000, mid-fidelity particle-in-cell simulations modeling instances of Target-Normal Sheath Acceleration. The neural-network-based function allows us to rapidly explore regions of interest in search of optimal input parameters and features of interest.
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Blagoje Z. Djordjevic, Andreas Kemp, Joohwan Kim, Scott Wilks, Raspberry Simpson, Tammy Ma, and Derek Mariscal "Parameter space exploration of short-pulse laser-driven ion acceleration via ensemble simulations and neural networks", Proc. SPIE 11666, High Power Lasers for Fusion Research VI, 116660E (5 March 2021); https://doi.org/10.1117/12.2587014
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KEYWORDS
Ions
Neural networks
Ion lasers
Optical simulations
Analytical research
Computer simulations
Diagnostics
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