透视:应用力学中的大语言模型

IF 2.6 4区 工程技术 Q2 MECHANICS Journal of Applied Mechanics-Transactions of the Asme Pub Date : 2023-06-21 DOI:10.1115/1.4062773
Neal R. Brodnik, Samuel Carton, Caelin Muir, Satanu Ghosh, Doug Downey, M. Echlin, T. Pollock, S. Daly
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引用次数: 2

摘要

大型语言模型(LLM),如ChatGPT和PaLM,能够在很少或没有训练的情况下执行复杂的文本理解和生成任务。除了更广泛的社会影响外,这些能力对包括应用力学在内的物理科学也有很大的前景。我们总结了这些模型的最新发展,它们在力学和邻近领域的应用,并展望了它们在应用力学中的未来应用,同时考虑到它们的局限性和该领域的独特挑战。
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Perspective: Large Language Models in Applied Mechanics
Large language models (LLMs), such as ChatGPT and PaLM, are able to perform sophisticated text comprehension and generation tasks with little or no training. Alongside their broader societal impacts, these capabilities carry great promise for the physical sciences, including applied mechanics. We present a summary of recent developments in these models, their application to mechanics and adjacent fields, and a perspective on their future use in applied mechanics, taking into account their limitations and the unique challenges of the field.
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来源期刊
CiteScore
4.80
自引率
3.80%
发文量
95
审稿时长
5.8 months
期刊介绍: All areas of theoretical and applied mechanics including, but not limited to: Aerodynamics; Aeroelasticity; Biomechanics; Boundary layers; Composite materials; Computational mechanics; Constitutive modeling of materials; Dynamics; Elasticity; Experimental mechanics; Flow and fracture; Heat transport in fluid flows; Hydraulics; Impact; Internal flow; Mechanical properties of materials; Mechanics of shocks; Micromechanics; Nanomechanics; Plasticity; Stress analysis; Structures; Thermodynamics of materials and in flowing fluids; Thermo-mechanics; Turbulence; Vibration; Wave propagation
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