Agentic Scientific Simulation: Execution-Grounded Model Construction and Reconstruction

2026-02-27

AI Summary

This work addresses the ambiguity in physics-based simulations described in natural language, which often leads to modeling inconsistencies that compromise correctness and reproducibility. To tackle this challenge, the authors propose a closed-loop modeling framework centered on simulator validation, employing an “interpret–execute–verify” cycle that integrates document retrieval, code generation, static analysis, and solver diagnostics to explicitly identify and resolve modeling uncertainties. Built upon the differentiable Julia simulator JutulDarcy, the intelligent agent JutulGPT enables explicit logging and interactive refinement of modeling assumptions. The study further introduces a novel approach to reconstruct reference models directly from textual descriptions to audit reproducibility. All code, prompts, and logs are publicly released, and experiments demonstrate the framework’s effectiveness in uncovering hidden degrees of freedom introduced by default parameters, establishing a verifiable and traceable paradigm for scientific modeling.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

LLM agents are increasingly used for code generation, but physics-based simulation poses a deeper challenge: natural-language descriptions of simulation models are inherently underspecified, and different admissible resolutions of implicit choices produce physically valid but scientifically distinct configurations. Without explicit detection and resolution of these ambiguities, neither the correctness of the result nor its reproducibility from the original description can be assured. This paper investigates agentic scientific simulation, where model construction is organized as an execution-grounded interpret-act-validate loop and the simulator serves as the authoritative arbiter of physical validity rather than merely a runtime. We present JutulGPT, a reference implementation built on the fully differentiable Julia-based reservoir simulator JutulDarcy. The agent combines structured retrieval of documentation and examples with code synthesis, static analysis, execution, and systematic interpretation of solver diagnostics. Underspecified modelling choices are detected explicitly and resolved either autonomously (with logged assumptions) or through targeted user queries. The results demonstrate that agent-mediated model construction can be grounded in simulator validation, while also revealing a structural limitation: choices resolved tacitly through simulator defaults are invisible to the assumption log and to any downstream representation. A secondary experiment with autonomous reconstruction of a reference model from progressively abstract textual descriptions shows that reconstruction variability exposes latent degrees of freedom in simulation descriptions and provides a practical methodology for auditing reproducibility. All code, prompts, and agent logs are publicly available.

Problem

• scientific simulation
• underspecification
• reproducibility
• model ambiguity
• physics-based simulation

Innovation

• agentic simulation
• execution-grounded modeling
• ambiguity resolution
• differentiable simulator
• reproducibility auditing