{"ID":2891629,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.16227","arxiv_id":"2507.16227","title":"Predictive Hydrodynamic Simulations for Laser Direct-drive Implosion Experiments via Artificial Intelligence","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.","short_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...","url_abs":"https://arxiv.org/abs/2507.16227","url_pdf":"https://arxiv.org/pdf/2507.16227v1","authors":"[\"Zixu Wang\",\"Yuhan Wang\",\"Junfei Ma\",\"Fuyuan Wu\",\"Junchi Yan\",\"Xiaohui Yuan\",\"Zhe Zhang\",\"Jie Zhang\"]","published":"2025-07-22T04:57:40Z","proceeding":"physics.plasm-ph","tasks":"[\"physics.plasm-ph\",\"cs.AI\"]","methods":"[\"Transformer\"]","has_code":false}