Predictive Hydrodynamic Simulations for Laser Direct-drive Implosion Experiments via Artificial Intelligence

• URL: http://arxiv.org/abs/2507.16227v1
• Date: Tue, 22 Jul 2025 04:57:40 GMT
• Title: Predictive Hydrodynamic Simulations for Laser Direct-drive Implosion Experiments via Artificial Intelligence
• Authors: Zixu Wang, Yuhan Wang, Junfei Ma, Fuyuan Wu, Junchi Yan, Xiaohui Yuan, Zhe Zhang, Jie Zhang,
• Abstract summary: This work presents predictive hydrodynamic simulations empowered by artificial intelligence (AI) for laser driven implosion experiments.<n>A Transformer-based deep learning model MULTI-Net is established to predict implosion features according to laser waveforms and target radius.<n>This study demonstrates a data-driven AI framework that enhances the prediction ability of simulations for complicated laser fusion experiments.
• Score: 45.84035159987504
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work presents predictive hydrodynamic simulations empowered by artificial intelligence (AI) for laser driven implosion experiments, taking the double-cone ignition (DCI) scheme as an example. A Transformer-based deep learning model MULTI-Net is established to predict implosion features according to laser waveforms and target radius. A Physics-Informed Decoder (PID) is proposed for high-dimensional sampling, significantly reducing the prediction errors compared to Latin hypercube sampling. Applied to DCI experiments conducted on the SG-II Upgrade facility, the MULTI-Net model is able to predict the implosion dynamics measured by the x-ray streak camera. It is found that an effective laser absorption factor about 65\% is suitable for the one-dimensional simulations of the DCI-R10 experiments. For shot 33, the mean implosion velocity and collided plasma density reached 195 km/s and 117 g/cc, respectively. This study demonstrates a data-driven AI framework that enhances the prediction ability of simulations for complicated laser fusion experiments.

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