Multi-Agent Design Assistant for the Simulation of Inertial Fusion Energy
🤖 AI Summary
Addressing the challenge of autonomous design for inertial fusion fuel capsules under extreme spatiotemporal scales, this paper introduces the first multi-agent inverse design framework integrating AI reasoning models with high-fidelity multiphysics simulations. The framework employs natural-language-driven reasoning for task planning and tightly couples physics-based simulators—such as radiation hydrodynamics, heat conduction, and neutron transport solvers—to jointly optimize capsule geometry and ignition trajectories. Its key innovation lies in embedding large language models’ symbolic reasoning capabilities directly into an executable simulation-in-the-loop architecture, enabling constraint-aware, autonomous iterative design. Experiments demonstrate that the system autonomously generates capsule configurations satisfying ignition thresholds—without human intervention—and high-fidelity simulations confirm their physical feasibility. This work establishes a new paradigm for automated, physics-guided design in fusion energy research.
Application Category
📝 Abstract
Inertial fusion energy promises nearly unlimited, clean power if it can be achieved. However, the design and engineering of fusion systems requires controlling and manipulating matter at extreme energies and timescales; the shock physics and radiation transport governing the physical behavior under these conditions are complex requiring the development, calibration, and use of predictive multiphysics codes to navigate the highly nonlinear and multi-faceted design landscape. We hypothesize that artificial intelligence reasoning models can be combined with physics codes and emulators to autonomously design fusion fuel capsules. In this article, we construct a multi-agent system where natural language is utilized to explore the complex physics regimes around fusion energy. The agentic system is capable of executing a high-order multiphysics inertial fusion computational code. We demonstrate the capacity of the multi-agent design assistant to both collaboratively and autonomously manipulate, navigate, and optimize capsule geometry while accounting for high fidelity physics that ultimately achieve simulated ignition via inverse design.
Problem
Research questions and friction points this paper is trying to address.
Developing AI-driven autonomous design for fusion fuel capsules
Combining reasoning models with multiphysics codes for fusion systems
Optimizing capsule geometry through multi-agent inverse design simulation
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-agent system using natural language for fusion exploration
AI reasoning combined with physics codes for autonomous design
Inverse design optimization achieving simulated ignition via high-fidelity physics
🔎 Similar Papers
2024-08-31arXiv.orgCitations: 0
