Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.16227 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918101350612992 |
|---|---|
| author | Wang, Zixu Wang, Yuhan Ma, Junfei Wu, Fuyuan Yan, Junchi Yuan, Xiaohui Zhang, Zhe Zhang, Jie |
| author_facet | Wang, Zixu Wang, Yuhan Ma, Junfei Wu, Fuyuan Yan, Junchi Yuan, Xiaohui Zhang, Zhe Zhang, Jie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16227 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predictive Hydrodynamic Simulations for Laser Direct-drive Implosion Experiments via Artificial Intelligence Wang, Zixu Wang, Yuhan Ma, Junfei Wu, Fuyuan Yan, Junchi Yuan, Xiaohui Zhang, Zhe Zhang, Jie Plasma Physics Artificial Intelligence 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. |
| title | Predictive Hydrodynamic Simulations for Laser Direct-drive Implosion Experiments via Artificial Intelligence |
| topic | Plasma Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2507.16227 |