MOOSEnger: A Simulation-Aware AI Agent Framework for the MOOSE Ecosystem
MOOSEnger is a modeling and simulation AI agent framework for the Multiphysics Object-Oriented Simulation Environment (MOOSE) ecosystem, built around a simulation-aware harness that combines an interchangeable reasoning model with grounded domain knowledge, revised simulation artifacts, MOOSE-specific validation, and executable solver feedback. This surrounding system addresses a central limitation of one-shot large language model generation: small syntax, schema, reference, or solver-configuration errors can prevent a plausible input from executing, while successful execution alone does not establish scientific correctness. MOOSEnger's simulation-aware harness integrates MOOSE knowledge ...
| Authors | Mengnan Li, Jason Miller, Zaid Abulawi et al. |
|---|---|
| Primary category | cs.AI |
| Published | 5 March 2026 |
| Last updated at source | 23 September 2026 |
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