The physical design of the fusion target is an important part of controlled thermonuclear fusion, and the geometric model and material selection of the target is also critical to achieving fusion ignition. We have modularised the target and introduced digital modeling, edge computing, and deep learning technologies to build a data-driven hybrid computing framework. We construct physical models and integrate them into a unified digital model of a multi-domain system; simulate and iteratively modify the physical model, and use edge computing technologies for information modeling. Edge computing is well applied to the calculation of each module of the target. Each module is both correlated and independent, and the values of the fusion ignition temperature and density achieved in the target are obtained, and the neutron products in the ignition and main fuel regions are 1016 - 1017 and 1019 respectively. This will be an important reference value for the design of actual fusion targets.

Volume ignition is a method of igniting a fuel as a whole by simultaneously achieving ignition conditions throughout the fuel zone. The basic criterion for ignition is that the thermonuclear energy is greater than the energy leakage at the fuel boundary, resulting in self-sustaining heating and deep combustion. Deuterium-tritium fuels are wrapped in medium to high Z media to reduce radiative leakage and achieve lower-temperature holistic ignition and non-equilibrium combustion, ultimately allowing the fuel to achieve high combustion efficiency. Volume ignition is the use of energy balance relations under the local thermodynamic equilibrium approximation to establish the energy balance equation for thermonuclear systems, and the system ignition threshold is obtained by solving this equation. By understanding the physical process, we believe that the non-equilibrium process is universal to the volume ignition process. Changes in external factors (density, boundaries, albedo, etc.) at the moment of ignition can have a significant impact on the development direction of the system, and an ignition system with a large surface density can nevertheless withstand a large amount of reverse work and continue to burn. The design of the ignition target tries to avoid these factors through margin design, but conversely, the rational use of these laws can further improve the design margin of the capsule. With the aid of big data, the volume ignition method is easier to calculate and has a shorter iteration time. The traditional way is to propose a model, set the material, and then perform the calculation while using big data can set any model and material for calculation. In this paper, a simple comparison will be made to find out that the efficiency of the physical design of a volume ignition target will be effectively improved with the aid of big data. Volume ignition targets can be used not only in Z-pinch-driven systems but also for laser-driven volume ignition.

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