MOOSEnger -- a Domain-Specific AI Agent for the MOOSE Ecosystem
#MOOSEnger #AI agent #MOOSE ecosystem #domain-specific #scientific computing #framework #automation
📌 Key Takeaways
- MOOSEnger is a specialized AI agent designed for the MOOSE ecosystem.
- It focuses on domain-specific tasks within the MOOSE framework.
- The agent aims to enhance efficiency and capabilities in MOOSE-related applications.
- This development represents an integration of AI into scientific computing tools.
📖 Full Retelling
arXiv:2603.04756v1 Announce Type: new
Abstract: MOOSEnger is a tool-enabled AI agent tailored to the Multiphysics Object-Oriented Simulation Environment (MOOSE). MOOSE cases are specified in HIT ".i" input files; the large object catalog and strict syntax make initial setup and debugging slow. MOOSEnger offers a conversational workflow that turns natural-language intent into runnable inputs by combining retrieval-augmented generation over curated docs/examples with deterministic, MOOSE-aware pa
🏷️ Themes
AI Agents, Scientific Computing
📚 Related People & Topics
AI agent
Systems that perform tasks without human intervention
In the context of generative artificial intelligence, AI agents (also referred to as compound AI systems or agentic AI) are a class of intelligent agents distinguished by their ability to operate autonomously in complex environments. Agentic AI tools prioritize decision-making over content creation ...
Entity Intersection Graph
Connections for AI agent:
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OpenAI
6 shared
🌐
Large language model
4 shared
🌐
Reinforcement learning
3 shared
🌐
OpenClaw
3 shared
🌐
Artificial intelligence
2 shared
Mentioned Entities
Original Source
--> Computer Science > Artificial Intelligence arXiv:2603.04756 [Submitted on 5 Mar 2026] Title: MOOSEnger -- a Domain-Specific AI Agent for the MOOSE Ecosystem Authors: Mengnan Li , Jason Miller , Zachary Prince , Alexander Lindsay , Cody Permann View a PDF of the paper titled MOOSEnger -- a Domain-Specific AI Agent for the MOOSE Ecosystem, by Mengnan Li and 4 other authors View PDF HTML Abstract: MOOSEnger is a tool-enabled AI agent tailored to the Multiphysics Object-Oriented Simulation Environment . MOOSE cases are specified in HIT ".i" input files; the large object catalog and strict syntax make initial setup and debugging slow. MOOSEnger offers a conversational workflow that turns natural-language intent into runnable inputs by combining retrieval-augmented generation over curated docs/examples with deterministic, MOOSE-aware parsing, validation, and execution tools. A core-plus-domain architecture separates reusable agent infrastructure (configuration, registries, tool dispatch, retrieval services, persistence, and evaluation) from a MOOSE plugin that adds HIT-based parsing, syntax-preserving ingestion of input files, and domain-specific utilities for input repair and checking. An input precheck pipeline removes hidden formatting artifacts, fixes malformed HIT structure with a bounded grammar-constrained loop, and resolves invalid object types via similarity search over an application syntax registry. Inputs are then validated and optionally smoke-tested with the MOOSE runtime in the loop via an MCP-backed execution backend (with local fallback), translating solver diagnostics into iterative verify-and-correct updates. Built-in evaluation reports RAG metrics (faithfulness, relevancy, context precision/recall) and end-to-end success by actual execution. On a 125-prompt benchmark spanning diffusion, transient heat conduction, solid mechanics, porous flow, and incompressible Navier--Stokes, MOOSEnger achieves a 0.93 execution pass rate versus 0.08 for an LLM-onl...
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