Physics > Applied Physics
arXiv:2510.17830 (physics)
[Submitted on 2 Oct 2025 (v1), last revised 21 Sep 2026 (this version, v3)]
Authors:Meir H. Shachar (1), Dane M. Sterbentz (1), Harshitha Menon (1), Charles F. Jekel (1), M. Giselle Fernández-Godino (1), Nathan K. Brown (2), Ismael D. Boureima (3), Yue Hao (1), Kevin Korner (1), Robert Rieben (1), Daniel A. White (1), William J. Schill (1), Jonathan L. Belof (1) ((1) Lawrence Livermore National Laboratory Livermore, CA, USA, (2) Sandia National Laboratories Albuquerque, NM, USA (3) Los Alamos National Laboratory Los Alamos, NM, USA)
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 translate natural-language design goals into simulation execution, physics-emulator construction, analysis, and iterative refinement of capsule-geometry parameters, ultimately achieving simulated ignition within the computational design workflow.
Submission history
From: Meir Shachar [view email]
[v1]
Thu, 2 Oct 2025 19:15:09 UTC (7,105 KB)
[v2]
Wed, 22 Oct 2025 01:49:42 UTC (7,105 KB)
[v3]
Mon, 21 Sep 2026 21:00:45 UTC (1,224 KB)
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