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arXiv AI By Harshitha Menon, Charles F. Jekel, Kevin Korner, M. Giselle Fernandez-Godino, Brian Gunnarson, Nathan K. Brown, Michael Stees, Walter Nissen, Meir H. Shachar, Dane M. Sterbentz, William J. Schill, Yue Hao, Robert Rieben, William Quadros, Steve Owen, Scott Mitchell, Ismael D. Boureima, Jonathan L. Belof

Multi-Agent Collaboration for Automated Design Exploration on High Performance Computing Systems

Read the original on arXiv AI →

The paper introduces MADA, a Large Language Model–powered multi‑agent framework that coordinates specialized agents—Job Management, Geometry, and Inverse Design—to automate complex design workflows on high‑performance computing systems. In the context of Richtmyer–Meshkov Instability suppression for Inertial Confinement Fusion, MADA iteratively refines designs by launching ensemble simulations, generating meshes, and proposing new designs based on simulation outcomes, achieving improved suppression with minimal manual effort. The framework demonstrates how coordinated reasoning, simulation, and specialized tools can be scaled for rapid, automated design exploration.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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