MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge

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Improved Baselines with Visual Instruction Tuning

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The rise and potential of large language model based agents: a survey

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Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design

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OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language ModelsPreprint

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Code Llama: Open Foundation Models for Code

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De novo composite design based on machine learning algorithm

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Deep learning model to predict complex stress and strain fields in hierarchical composites

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The Finite Volume Method

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AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

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Finite element analysis of natural fibers composites: A review

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End-to-end deep learning method to predict complete strain and stress tensors for complex hierarchical composite microstructures

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MechGPT, a Language-Based Strategy for Mechanics and Materials Modeling That Connects Knowledge Across Scales, Disciplines, and Modalities

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Nature-inspired architected materials using unsupervised deep learning

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ChatGPT FOR PROGRAMMING NUMERICAL METHODS

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Finite Elements in Fracture Mechanics : Theory - Numerics - Applications

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Generative design of de novo proteins based on secondary-structure constraints using an attention-based diffusion model

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Generative Retrieval-Augmented Ontologic Graph and Multiagent Strategies for Interpretive Large Language Model-Based Materials Design

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A deep learning approach to the inverse problem of modulus identification in elasticity

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Single-shot forward and inverse hierarchical architected materials design for nonlinear mechanical properties using an Attention-Diffusion model

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MeLM, a generative pretrained language modeling framework that solves forward and inverse mechanics problems

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FieldPerceiver: Domain agnostic transformer model to predict multiscale physical fields and nonlinear material properties through neural ologs

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Modeling Atomistic Dynamic Fracture Mechanisms Using a Progressive Transformer Diffusion Model

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Community action on FAIR data will fuel a revolution in materials research

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A deep learning augmented genetic algorithm approach to polycrystalline 2D material fracture discovery and design

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Unlocking the secrets of locking: Finite element analysis in planar linear elasticity

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CollagenTransformer: End-to-End Transformer Model to Predict Thermal Stability of Collagen Triple Helices Using an NLP Approach

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Transition from source- to stress-controlled plasticity in nanotwinned materials below a softening temperature

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Predicting mechanical fields near cracks using a progressive transformer diffusion model and exploration of generalization capacity

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Bayesian optimisation of hexagonal honeycomb metamaterial

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Generative pretrained autoregressive transformer graph neural network applied to the analysis and discovery of novel proteins

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An automated biomateriomics platform for sustainable programmable materials discovery

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An accurate, robust, and efficient finite element framework with applications to anisotropic, nearly and fully incompressible elasticity

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Polymer informatics beyond homopolymers

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Deep learning virtual indenter maps nanoscale hardness rapidly and non-destructively, revealing mechanism and enhancing bioinspired design

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Megalibraries: Supercharged acceleration of materials discovery

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Mathematical nuances of Gaussian process-driven autonomous experimentation

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Tuning crack-inclusion interaction with an applied $$\varvec{T}$$-stress

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Adaptive Expansion Bayesian Optimization for Unbounded Global OptimizationPreprint

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Architected Materials for Mechanical Compression: Design via Simulation, Deep Learning, and ExperimentationPreprint

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ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion modelPreprint

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The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges

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SciAgents: Automating Scientific Discovery Through Bioinspired Multi‐Agent Intelligent Graph Reasoning

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Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap

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ProtAgents: protein discovery via large language model multi-agent collaborations combining physics and machine learning

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A survey of emerging applications of large language models for problems in mechanics, product design, and manufacturing

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Automating alloy design and discovery with physics-aware multimodal multiagent AI

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LLM-Based Agents for Tool Learning: A Survey

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OpenFOAMGPT: A retrieval-augmented large language model (LLM) agent for OpenFOAM-based computational fluid dynamics

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SciToolAgent: a knowledge-graph-driven scientific agent for multitool integration

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Offshore wind turbine tower design and optimization: A review and AI-driven future directions

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A Review of AI-Driven Automation Technologies: Latest Taxonomies, Existing Challenges, and Future Prospects

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Cephalo: Multi‐Modal Vision‐Language Models for Bio‐Inspired Materials Analysis and Design

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Multimodal Transformer for Property Prediction in Polymers

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Deep learning surrogate for phase field modeling in fiber-reinforced composites: From stress evolution to final crack path

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Roadmap on data-centric materials science

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A Systematic Survey on Large Language Models for Algorithm Design

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End-to-end prediction and design of additively manufacturable alloys using a generative AlloyGPT model

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Facilitating or Inhibiting: A Study on the Impact of Artificial Intelligence on Corporate Greenwashing

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Molecular analysis and design using generative artificial intelligence via multi-agent modeling

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Roadmap on Artificial Intelligence‐Augmented Additive Manufacturing

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A Comparative Review of Large Language Models in Engineering with Emphasis on Chemical Engineering Applications

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Can ChatGPT Implement Finite Element Models for Geotechnical Engineering Applications?

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AI-powered language models for alloy design and laser-based manufacturing: A review of NLP applications in materials science

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How Does a Generative Large Language Model Perform on Domain-Specific Information Extraction?─A Comparison between GPT-4 and a Rule-Based Method on Band Gap Extraction

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Large language models for material property predictions: elastic constant tensor prediction and materials design

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From large language models to AI agents in energy materials research: enabling discovery, design, and automation

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Integrating large language models, reinforcement learning, and machine learning for intelligent indoor thermal comfort regulation

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Rapid and automated alloy design with graph neural network-powered large language model-driven multi-agent AI

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Knowledge-extractor: a self-evolving scientific framework for hydrogen energy research driven by AI agents

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Nanostructured Material Design via a Retrieval-Augmented Generation (RAG) Approach: Bridging Laboratory Practice and Scientific Literature

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Agents are All you need: Elevating Trading Dynamics with Advanced Generative AI-Driven Conversational LLM Agents and Tools

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A MISLEADING GALLERY OF FLUID MOTION BY GENERATIVE ARTIFICIAL INTELLIGENCE

Kashefi, A. (2024) · Journal of Machine Learning for Modeling and Computing · doi:10.1615/jmachlearnmodelcomput.2024054191 · Cited by 10 · see citing works

Plurals: A System for Guiding LLMs via Simulated Social Ensembles

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A self-correcting multi-agent LLM framework for language-based physics simulation and explanation

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Integrating topology optimization into model-Based systems engineering for lightweight structural design

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CST-LLM: Enhancing airfoil parameterization method with large language model

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Collaborative Tree Search for Enhancing Embodied Multi-Agent Collaboration

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Automatically Differentiable Model Updating (ADiMU): Conventional, hybrid, and neural network material model discovery including history-dependency

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A Vehicle-Infrastructure Multi-Layer Cooperative Decision-Making Framework

Cui, Y., Fang, S., Hang, P., & Sun, J. (2025) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv64158.2025.11097727 · Cited by 6 · see citing works

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

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Large language model driven development of turbulence models

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Agentic material science

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Physics-constrained machine learning for constitutive modelling of high-strength concrete across strain rates

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Large language models in mechanical design of mechatronic systems: A review

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Intelligent Decision‐Making Driven by Large AI Models: Progress, Challenges and Prospects

He, Y., Ruan, S., Wang, D. J., Lu, H., Li, Z., Liu, Y., Xu, C. G., Li, S., Zhao, J., & Liang, J. (2025) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.70084 · Cited by 4 · see citing works

AutoBnB-RAG: Enhancing Multi-Agent Incident Response with Retrieval-Augmented Generation

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Toward Sustainable Agentic AI Systems: A Survey of Architectures and Methodologies

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Self-revising discovery systems for science: A categorical framework for agentic artificial intelligence

Wang, F. Y., & Buehler, M. J. (2026) · Extreme Mechanics Letters · doi:10.1016/j.eml.2026.102518 · Cited by 4 · see citing works

Human–AI Learning: Architecture of a Human–AgenticAI Learning System

Williams, P. (2025) · Information · doi:10.3390/info16121101 · Cited by 8 · see citing works

Intelligent Recognition of Tool Wear with Artificial Intelligence Agent

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Molecular analysis and design using multimodal generative artificial intelligence via multi-agent modelingPreprint

Stewart, I., & Buehler, M. J. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2024-nwm7n · Cited by 4 · see citing works

Using Role-Specialized LLM Agents for Workflow Execution in a Product Development Setting

Kretzschmar, M., Gundermann, A., Mehlstäubl, J., Gómez, A. P., Saske, B., & Paetzold-Byhain, K. (2025) · International Conference on Inventive Systems and Control (ICISC) · doi:10.1109/icisc65841.2025.11188152 · Cited by 3 · see citing works

Creation, evaluation and self-validation of simulation models with large language models

Möltner, T., Manzl, P., Pieber, M., & Gerstmayr, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132030 · Cited by 3 · see citing works

AirfoilRAG: Retrieval augmented generation framework for airfoil aerodynamic design knowledge discovery and application

Fan, Y., Shi, L., Sun, Y., & Mi, B. (2025) · Aerospace Science and Technology · doi:10.1016/j.ast.2025.110933 · Cited by 3 · see citing works

TopoMAS: Large Language Model Driven Topological Materials Multi‐Agent System

Zhang, B., Li, X., Xu, H., Jin, Z., Wu, Q., & Li, C. (2026) · Materials Genome Engineering Advances · doi:10.1002/mgea.70045 · Cited by 8 · see citing works

VibeGen: Agentic end-to-end de novo protein design for tailored dynamics using a language diffusion model

Ni, B., & Buehler, M. J. (2026) · Matter · doi:10.1016/j.matt.2026.102706 · Cited by 7 · see citing works

Data-driven representative models to accelerate scaled-up atomistic simulations of bitumen and biobased complex fluids

York, D., Vidal, I., Segura, C., Norambuena-Contreras, J., & Martín‐Martínez, F. J. (2024) · Digital Discovery · doi:10.1039/d3dd00245d · Cited by 3 · see citing works

Advanced Mechanics of Hard Tissue Using Imaging-Based Measurements and Artificial Intelligence

Tozzi, G., & Buehler, M. J. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-90646-3.00046-0 · Cited by 3 · see citing works

A Multimodal Agent Framework for Construction Scenarios: Accurate Perception, Dynamic Retrieval, and Explainable Hazard Reasoning

Cheng, S., Qi, Y., Wu, R., & Guan, Y. (2025) · Buildings · doi:10.3390/buildings15244439 · Cited by 2 · see citing works

GrapheneChat: A Large Language Model for Enhancing Graphene Research

Yang, D., Lu, C., Feng, Y., Mao, W., Wang, K., Sun, L., Wang, Y., Hu, B., Tan, J., Liu, Z., et al. (2026) · ACS Nano · doi:10.1021/acsnano.5c21335 · Cited by 2 · see citing works

Adversarial robustness of LLM-based multi-agent systems for engineering problems

Wiesmeier, L., Busch, M., Tacke, M., Linka, K., Cyron, C. J., & Aydin, R. C. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1784484 · Cited by 2 · see citing works

Why We Must Break the World

Buehler, M. J. (2026) · Integrating materials and manufacturing innovation · doi:10.1007/s40192-026-00465-2 · Cited by 2 · see citing works

Humanity Amplified: The Fusion of Deep Learning and Human Insight to Shape the Future of InnovationPreprint

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Cooperative Ai Agents Using Supervised Global Workspaces

Aarabi, P., & Qin, Y. (2025) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai64502.2025.00056 · Cited by 1 · see citing works

Automating data-driven modeling and analysis for engineering applications using large language model agents

Liu, Y., Abulawi, Z., Garimidi, A., & Lim, D. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115989 · Cited by 1 · see citing works

Impact of generative artificial intelligence on workload, efficiency and labour productivity

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Dynamic Consensus Communication Mechanism for Large Language Model-Based Multi-Agent Systems

Yang, L., Li, S., & Deng, A. (2026) · Journal of Signal Processing Systems · doi:10.1007/s11265-025-01978-3 · Cited by 1 · see citing works

How does contextual fidelity impact how we think, talk, and act in AI-assisted engineering design?

Vyas, S., Cheruku, S., & Krishnamurthy, V. R. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.104456 · Cited by 1 · see citing works

InsQABench: Benchmarking Chinese insurance domain question answering with large language models

Wei, L., Ding, J., Feng, K., Lin, B., Cai, J., Wang, Q., Xie, Y., Zhang, X., Wei, Z., & Chen, W. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.104685 · Cited by 3 · see citing works

Towards intelligent safety assessment under blast loading: A large language model-powered multi-agent framework

Wang, L., Yan, J., Yan, J., & Liu, Y. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112560 · Cited by 1 · see citing works

ActivityRDI: A Centralized Solution Framework for Activity Retrieval and Detection Intelligence Based on Knowledge Graphs, Large Language Models, and Imbalanced Learning

Zhang, L., & Zhu, Q. (2026) · Machine Learning and Knowledge Extraction · doi:10.3390/make8030075 · Cited by 1 · see citing works

Computational mechanics empowered by artificial intelligence

Chinesta, F., Baranger, E., Jailin, C., Barasinski, A., Cinnella, P., & Feyel, F. (2026) · Mechanics & Industry · doi:10.1051/meca/2026022 · Cited by 1 · see citing works

LLMs as designers of physics-constrained neural networks for constitutive modeling: a demonstration on hyperelastic solids

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CoLMAgent: A collaborative agentic AI system that integrates large and small models for industrial intelligent human–machine interactions

Li, N., Xie, G., Shi, H. Z., Nie, X., & Wang, Y. (2026) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2026.06.020 · Cited by 1 · see citing works

Battery-Sim-Agent: Leveraging LLM-Agent for Inverse Battery Parameter Estimation

Chen, J., Gui, X., Fang, S., Tao, S., Zheng, S., Liu, W., & Bian, J. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3818856 · Cited by 1 · see citing works

AudioFab: Building A General and Intelligent Audio Factory through Tool Learning

Zhu, C., Han, J., Xue, Q., Wang, K., Zhao, H., & Zhang, Z. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3756869

Designing an Educational Virtual Assistant Based on Agentic AI for Student Diagnosis and Individualized Remediation

Fahmi, Y., & Bousmah, M. (2025) · IEEE Congress on Information Science and Technology (CiSt) · doi:10.1109/cist65886.2025.11224281

Large Language Model Driven Development of Turbulence Models

Yang, Z., Bin, Y., Shi, Y., & Yang, X. I. A. (2026) · AIAA SciTech Forum · doi:10.2514/6.2026-0291

Physics-aware emergent cognition for discovery, materials innovation, and design through agentic modeling

Buehler, M. J. (2026) · Nano Futures · doi:10.1088/2399-1984/ae4152 · Cited by 2 · see citing works

Symmetry-based authority allocation for enhanced multi-agent decision-making

Fang, Z., & Huang, L. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.113949

Aligning Agents With Experts in Material Selection: Prompting, Size, and Reasoning

Ying, M. Y., Grandi, D., Groom, A., & McComb, C. (2026) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4071467

Application of a Bayesian Belief of Probability Multivariate Time Series Approach for Detecting Potential Cheating in Handwritten Online Engineering Exams

Ramnath, V. (2026) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa67686.2026.11468222

Application of multi-agent in energy materials research

Wang, J., Zhu, H., Li, P., Liu, C., Liu, Y., & Wan, J. (2026) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/csb-2025-5793

DeepSeek and multimodal AI in healthcare: enabling precision diagnosis and smart clinical decision support

Dhull, A., Singh, A., Vashist, S., Singh, V., & Singh, K. S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44025-0.00001-2

Conversational programming for structural model review and editing via conversational corrective grounding (CCG)

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AI Judges in Design: Toward Expert-Equivalent Design Evaluations With Vision-Language Models and In-Context Learning

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A typology of GenAI capabilities for business innovation

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PAMF: An LLM-driven framework for automated mesh generation in mechanical simulation and CAE workflows

Pan, F., Yin, C., & Dou, G. (2026) · Journal of Mechanical Science and Technology · doi:10.1007/s12206-026-0334-6

A large-language model multiagent framework for incorporating tolerancing decision-making into conceptual design

Wang, S., Liu, A., & Xu, X. (2026) · Procedia CIRP · doi:10.1016/j.procir.2026.05.242

An Agent-Verified System for Resource-Constrained Query-Focused Summarisation using Small Language ModelsPreprint

Majumder, S., & Sarkar, S. C. (2026) · Research Square · doi:10.21203/rs.3.rs-9986413/v1

Bio-inspired additive-manufactured metamaterials: structure, function, and design principles

Bhate, D., Mistry, Y., Yaple, J., Okun, Z., Martinez, I. C., Handel, N. V., Sarrasin, A., Nunez, M., Talange, A., Garg, K., et al. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33953-0.00015-6

Comparing teacher-centered and student-centered agents based on prompt engineering: Effects on learning performance, cognitive load, flow experience, and empathy perception in physics learning

Anonymous, Chen, X., Xiong, Y., Xu, S., Li, Q., & Zhou, S. (2026) · Physical Review Physics Education Research · doi:10.1103/9t5b-twsb

Towards efficient affinity scheduling for complex multi-agent systems in containerised cyber-physical environments

Guo, H., Dou, L., Jia, Z., & Gu, H. (2026) · Cyber-Physical Systems · doi:10.1080/23335777.2026.2702375

Embedding Bayesian optimization in a multi-agent large language model framework for critical heat flux modeling with uncertainty quantification

Liu, Y., Abulawi, Z., Garimidi, A., & Lim, D. (2026) · Nuclear Engineering and Technology · doi:10.1016/j.net.2026.104573

The Future of Mechanical Metastructures: There is Plenty of Room at the Top

Wu, W., Yu, H., Xu, K., Yang, H., Wang, H., Xu, F., & Lu, T. (2026) · ENGINEERING Transformative Materials · doi:10.2738/engtm.2026.0007

AlloyGen: A Physics-grounded Self-adaptive Multi-agent Framework for Autonomous Alloy Design Workflows in Additive ManufacturingPreprint

Taheri-Mousavi, S. M., & Ni, B. (2026) · Research Square · doi:10.21203/rs.3.rs-10329502/v1

PEARL: A Physics Enforcing Agentic Retraining Loop for Equation Discovery with KAN and MLP Architectures

Shetty, J., Patel, S., Rathod, D., Pandey, O., & Deshpande, H. S. (2026) · International Conference on Data Science for Cyber-Physical Systems Resilience using Advanced Applications (ICDCA) · doi:10.1109/icdca69396.2026.11620217

Swarms of large language model agents for protein sequence design with experimental validation

Wang, F. Y., Lee, D. S., Kaplan, D. L., & Buehler, M. J. (2026) · Digital Discovery · doi:10.1039/d6dd00282j · Cited by 3 · see citing works

A Hierarchical Multi-Agent System for Automated RF Circuit Design: A 2.4-GHz Chebyshev Filter Case Study

Jiang, H., 范光濠, Wang, H., Zhang, J., & Yan, X. (2026) · Cross Strait Radio Science and Wireless Technology Conference (CSRSWTC) · doi:10.1109/csrswtc71154.2026.11669836

A trustworthy agentic AI framework for auditable label-free machinery fault diagnosis in safety-critical engineering systems

Chen, Y., Liu, B., Li, Y., Wang, Z., Ma, L., Liu, J., Tian, X., & Huang, L. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.105205

WorldSolver: Can LLM Agents Simulate the Physical Dynamics via Solver Generation?Preprint

Jiang, S., Lyu, Y., Lu, S., Wang, Y., Zhang, Y., Liao, Y., Wang, B., Liang, J., & Tan, T. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2610.08720

FMAgent: Autonomous multi-agent system for fluid mechanics

Sha, H., Bao, B., Jin, X., Li, S., Zuo, W., & Li, H. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.104988

Optimizing Collaboration of Large Language Model Based Agents for Autonomous Finite Element AnalysisPreprint

Zhang, Y., & Tian, C. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5107557

Differential-agent: large language model-empowered multiagent for numerical analysis and simulation

Zheng, Z. (2026) · International Conference on Big Data, Computational Intelligence, and Applications (BDCIA) · doi:10.1117/12.3103315

A Self-Correcting Multi-Agent Framework for Language-Based Physics Simulation and Explanation

Park, D. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.15645332