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EQSIM Agent: A Conversational AI for Interactive Exploration of Large-scale Earthquake Simulation Data

Workshop: Frontiers in Generative AI for HPC Science and Engineering: Foundations, Challenges, and Opportunities

Authors: Houjun Tang and David McCallen (Lawrence Berkeley National Laboratory (LBNL))

Abstract: Large-scale earthquake simulations produce massive, high-fidelity datasets essential for seismic risk analysis, however, their volume and complexity create a barrier for researchers from various backgrounds who lack specialized knowledge and programming skills. To address this challenge, we leveraged Large Language Models (LLMs) to develop the EQSIM Agent, a conversational AI designed for the interactive exploration of large-scale earthquake simulation data. The agent allows users to query data using natural language, receiving results as text, images, videos, and maps. Beyond standard querying and visualization, it introduces novel features like a vision-based waveform similarity search and a Retrieval-Augmented Generation system that answers questions with facts from relevant publications. This paper details the agent’s implementation and evaluates the challenges of using LLMs in a scientific context. We also provide a practical analysis of various LLMs, evaluating their performance, tool-calling reliability, and cost, to guide the development of future scientific AI agents.

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