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

2025-07-22

AI Summary

This study addresses the low efficiency and insufficient accuracy of high-dimensional, strongly nonlinear hydrodynamic simulations in laser-driven inertial confinement fusion implosion experiments. We propose a physics-informed, end-to-end deep learning framework. Methodologically, we design a Transformer-based MULTI-Net model that integrates a physics-informed decoder with Latin hypercube sampling to enable high-fidelity prediction of implosion dynamics—including shock propagation, compressed density profiles, and velocity fields—from input laser waveforms. Our key contribution lies in embedding prior physical constraints directly into the decoding process, substantially enhancing generalization capability and prediction stability under limited training data. Validated on the SG-II Upgrade Facility’s Shot #33 double-cone ignition experiment, the model accurately reproduces X-ray streak camera observations, predicts an average implosion velocity of 195 km/s, a peak plasma density of 117 g/cm³, and corroborates a 65% laser absorption efficiency—establishing a robust, data-driven simulation paradigm for fusion target design.

Technology Category

• Machine Learning: Multimodal Learning
• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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.

Problem

• Predict implosion features using AI for laser-driven experiments
• Reduce prediction errors with Physics-Informed Decoder sampling
• Enhance simulation accuracy for complex laser fusion dynamics

Innovation

• Transformer-based MULTI-Net predicts implosion features
• Physics-Informed Decoder reduces prediction errors
• AI framework enhances laser fusion simulation accuracy