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

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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A Survey on Evaluation of Large Language Models

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Llama 2: Open Foundation and Fine-Tuned Chat Models

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Artificial neural networks for solving ordinary and partial differential equations

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A survey on large language model based autonomous agents

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Improved Baselines with Visual Instruction Tuning

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Applied Mechanics of Solids

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Sparks of Artificial General Intelligence: Early experiments with GPT-4

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

Bandi, A., Kongari, B., Naguru, R., Pasnoor, S., & Vilipala, S. V. (2025) · Future Internet · doi:10.3390/fi17090404 · Cited by 207

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

Ghafarollahi, A., & Buehler, M. J. (2024) · Advanced Materials · doi:10.1002/adma.202413523 · Cited by 330

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

Ghafarollahi, A., & Buehler, M. J. (2024) · Digital Discovery · doi:10.1039/d4dd00013g · Cited by 167

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

Mustapha, K. B. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.103066 · Cited by 107

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

Ghafarollahi, A., & Buehler, M. J. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2414074122 · Cited by 72

LLM-Based Agents for Tool Learning: A Survey

Xu, W., Huang, C., Gao, S., & Shang, S. (2025) · Data Science and Engineering · doi:10.1007/s41019-025-00296-9 · Cited by 87

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

Pandey, S., Xu, R., Wang, W., & Chu, X. (2025) · Physics of Fluids · doi:10.1063/5.0257555 · Cited by 87

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

Ding, K., Yu, J., Huang, J., Yang, Y., Zhang, Q., & Chen, H. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00849-y · Cited by 73

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

Ribeiro, J. A., Ribeiro, B. A., Pimenta, F., Tavares, S. M. O., Zhang, J., & Ahmed, F. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126294 · Cited by 44

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

Jin, W., Wang, N., Zhang, L., Tian, X., Shi, B., & Zhao, B. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.067857 · Cited by 48

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

Buehler, M. J. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202409531 · Cited by 43

Multimodal Transformer for Property Prediction in Polymers

Han, S., Kang, Y., Park, H., Yi, J., Park, G., & Kim, J. (2024) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.4c01207 · Cited by 36

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

Zhang, P., Xiang, Y., & Tang, K. (2025) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2025.118518 · Cited by 35

Roadmap on data-centric materials science

Bauer, S., Benner, P., Bereau, T., Blüm, V., Boley, M., Carbogno, C., Catlow, C. R. A., Dehm, G., Eibl, S., Ernstorfer, R., et al. (2024) · Modelling and Simulation in Materials Science and Engineering · doi:10.1088/1361-651x/ad4d0d · Cited by 32

A Systematic Survey on Large Language Models for Algorithm Design

Liu, F., Yao, Y., Guo, P., Yang, Z., Lin, X., Zhao, Z., Tong, X., Mao, K., Lu, Z., Wang, Z., et al. (2026) · ACM Computing Surveys · doi:10.1145/3787585 · Cited by 107

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

Ni, B., Glaser, B., & Taheri-Mousavi, S. M. (2025) · npj Computational Materials · doi:10.1038/s41524-025-01768-2 · Cited by 23

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

Tian, X., & Shi, D. (2025) · Sustainability · doi:10.3390/su17052154 · Cited by 34

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

Stewart, I., & Buehler, M. J. (2025) · Molecular Systems Design & Engineering · doi:10.1039/d4me00174e · Cited by 25

Roadmap on Artificial Intelligence‐Augmented Additive Manufacturing

Zolfagharian, A., Jin, L., Ge, Q., Liao, W., Lantada, A. D., Martínez, F. F., Zhang, T., Liu, T., Wang, C. C. L., Mosallanejad, M. H., et al. (2026) · Advanced Intelligent Systems · doi:10.1002/aisy.202500484 · Cited by 23

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

Khoo, T. L., Lee, T. S., Bee, S., Ma, C., & Zhang, Y.-Y. (2025) · Processes · doi:10.3390/pr13092680 · Cited by 20

Can ChatGPT Implement Finite Element Models for Geotechnical Engineering Applications?

Kim, T.-G., Yun, T. S., & Suh, H. S. (2025) · International Journal for Numerical and Analytical Methods in Geomechanics · doi:10.1002/nag.3956 · Cited by 16

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

Nabavi, S. F., Garmestani, H., & Fekri, F. (2025) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2025.11.035 · Cited by 19

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

Wang, X., Huang, L., Xu, S., & Lü, K. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00882 · Cited by 18

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

Liu, S., Wen, T., Ye, B., Li, Z., Liu, H., Ren, Y., & Srolovitz, D. J. (2025) · Digital Discovery · doi:10.1039/d5dd00061k · Cited by 17

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

Yao, T., Huang, J., Yan, Y., Yang, Y., Wang, Z., Shao, X., Gao, Z., & Yang, W. (2025) · AI Agent · doi:10.20517/aiagent.2025.03 · Cited by 14

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

Liu, D., Ling, F., Zhou, X., & Li, Y. (2025) · Architectural Science Review · doi:10.1080/00038628.2025.2488522 · Cited by 13

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

Ghafarollahi, A., & Buehler, M. J. (2025) · MRS Bulletin · doi:10.1557/s43577-025-00953-4 · Cited by 11

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

Yao, T., Yang, Y., Yan, Y., Ou, X., Li, M., Wang, C., Li, W., Du, C., Shao, X., Gao, Z., et al. (2025) · AI Agent · doi:10.20517/aiagent.2025.04 · Cited by 12

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

Krotkov, N. A., Sbytov, D. A., Chakhoyan, A. A., Kornienko, P. I., Starikova, A. A., Stepanov, M. G., Piven, A. O., Aliev, T. A., Orlova, T., Rafayelyan, M. S., et al. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01897 · Cited by 12

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

Kar, I., Ralte, Z., Shivakumara, M., Roy, R., & Kumari, A. (2024) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct61223.2024.10543356 · Cited by 12

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

Plurals: A System for Guiding LLMs via Simulated Social Ensembles

Ashkinaze, J., Fry, E., Edara, N., Gilbert, É., & Budak, C. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713675 · Cited by 24

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

Park, D., Moon, H., & Ryu, S. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-025-00057-z · Cited by 8

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

Guerrero, D. A. A., Jacobs, G., Zerwas, T., & Delgadillo, A. (2025) · Forschung im Ingenieurwesen · doi:10.1007/s10010-025-00829-8 · Cited by 7

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

zheng, K., Wang, Y., Liu, F., Zhang, Q., & Song, W. (2025) · Aerospace Science and Technology · doi:10.1016/j.ast.2025.110548 · Cited by 8

Collaborative Tree Search for Enhancing Embodied Multi-Agent Collaboration

Zu, L., Lin, L., Fu, S., Zhao, N., & Zhou, P. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02748 · Cited by 11

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

Ferreira, B. P., & Bessa, M. A. (2025) · Journal of the Mechanics and Physics of Solids · doi:10.1016/j.jmps.2025.106408 · Cited by 7

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

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

Tian, J. M., Sobczak, M. T., Patil, D., Hou, J., Pang, L., Ramanathan, A., Yang, L., Chen, X., Golan, Y., Zhaı, X., et al. (2025) · Advanced Intelligent Discovery · doi:10.1002/aidi.202500063 · Cited by 5

Large language model driven development of turbulence models

Yang, Z., Bin, Y., Shi, Y., & Yang, X. I. A. (2025) · Flow · doi:10.1017/flo.2025.10032 · Cited by 5

Agentic material science

Li, C., Ran, N., & Liu, J. (2026) · Journal of Materials Informatics · doi:10.20517/jmi.2025.87 · Cited by 7

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

Long, X., Steve, N. T. N. A., & Kawkabi, K. W. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114314 · Cited by 5

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

Zeman, Z., Mikkola, A., Orzechowski, G., Suder, J., & Mihola, M. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.104807 · Cited by 5

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 5

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

Liu, Z., & Anwar, A. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00141 · Cited by 7

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

Lee, Y., & Park, E. (2026) · Sustainable Development · doi:10.1002/sd.70942 · Cited by 6

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

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

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

Intelligent Recognition of Tool Wear with Artificial Intelligence Agent

Gao, J., Qiao, H., & Zhang, Y. (2024) · Coatings · doi:10.3390/coatings14070827 · Cited by 4

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

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 4

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 7

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

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 16

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 9

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

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 5

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 6

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

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

Why We Must Break the World

Buehler, M. J. (2026) · Integrating materials and manufacturing innovation · doi:10.1007/s40192-026-00465-2 · Cited by 3

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

Bauschard, S., & Quidwai, S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4621210 · Cited by 4

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

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 7

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

Caamaño-Gordillo, D., Mula, J., & de la Torre, R. (2025) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2025.09.237 · Cited by 3

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 2

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 4

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 6

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 2

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

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 2

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

Tacke, M., Busch, M., Abdolazizi, K. P., Eichinger, J., Linka, K., Cyron, C. J., & Aydin, R. C. (2026) · Machine learning for computational science and engineering · doi:10.1007/s44379-026-00073-z · Cited by 1

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

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

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

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)

Lee, J. S., & Lee, G. (2026) · Automation in Construction · doi:10.1016/j.autcon.2026.106989

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

Edwards, K. M., Tehranchi, F., Miller, S., & Ahmed, F. (2026) · Journal of Mechanical Design · doi:10.1115/1.4071835 · Cited by 1

A typology of GenAI capabilities for business innovation

Vanin, G. S., Cortimiglia, M. N., Ghezzi, A., & Nascimento, L. D. S. (2026) · Business Horizons · doi:10.1016/j.bushor.2026.05.002 · Cited by 2

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

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