A six-agent LLM system automates end-to-end Abaqus FEA for solid mechanics problems and achieves 86% success on 50 test cases.
Title resolution pending
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
roles
background 2polarities
background 2representative citing papers
SIGA is a coding-agent adapter using retrieval, procedural memory, and validation gates that raises success rate on GEOS from 0.720 to 0.789 while cutting variance 16x and matching expert quality in minutes instead of hours.
VFEAgent is an end-to-end multi-agent framework that automates FEA modeling and simulation from multimodal inputs, achieving high success rates in generating physically valid simulations across engineering scenarios.
An agentic AI workflow automates end-to-end SPH debris flow simulations via tool orchestration, multimodal inputs, and human-in-the-loop, demonstrating viability for meshless computational mechanics.
COSMO-Agent trains LLMs via tool-augmented RL and a multi-constraint reward to close the CAD-CAE loop, with experiments showing small open-source models outperforming larger ones on feasibility and stability for 25 component categories.
GenoMAS deploys six specialized LLM agents with guided planning to preprocess transcriptomic data and identify genes, reaching 89.13% composite similarity and 60.48% F1 on the GenoTEX benchmark while outperforming prior methods.
COSMO-Agent is a tool-augmented RL agent that trains LLMs to complete closed-loop CAD-CAE optimization using a multi-constraint reward and an industry dataset of 25 component categories, improving small models over larger ones.
citing papers explorer
-
A Multi-AI-agent Framework Enabling End-to-end Finite Element Analysis for Solid Mechanics Problems
A six-agent LLM system automates end-to-end Abaqus FEA for solid mechanics problems and achieves 86% success on 50 test cases.
-
Auto-Configuring Scientific Simulators with Lightweight Coding-Agent Adapters
SIGA is a coding-agent adapter using retrieval, procedural memory, and validation gates that raises success rate on GEOS from 0.720 to 0.789 while cutting variance 16x and matching expert quality in minutes instead of hours.
-
VFEAgent: A Multimodal Agent Framework for End-to-End Automated Finite Element Analysis
VFEAgent is an end-to-end multi-agent framework that automates FEA modeling and simulation from multimodal inputs, achieving high success rates in generating physically valid simulations across engineering scenarios.
-
Agentic AI for Particle-Based Simulation: Automating SPH Workflows for Debris Flow Modeling
An agentic AI workflow automates end-to-end SPH debris flow simulations via tool orchestration, multimodal inputs, and human-in-the-loop, demonstrating viability for meshless computational mechanics.
-
COSMO-Agent: Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration
COSMO-Agent trains LLMs via tool-augmented RL and a multi-constraint reward to close the CAD-CAE loop, with experiments showing small open-source models outperforming larger ones on feasibility and stability for 25 component categories.
-
GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis
GenoMAS deploys six specialized LLM agents with guided planning to preprocess transcriptomic data and identify genes, reaching 89.13% composite similarity and 60.48% F1 on the GenoTEX benchmark while outperforming prior methods.
-
Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration
COSMO-Agent is a tool-augmented RL agent that trains LLMs to complete closed-loop CAD-CAE optimization using a multi-constraint reward and an industry dataset of 25 component categories, improving small models over larger ones.