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Abstract The concept of big data with digital twin and artificial intelligence is profoundly influencing the fourth industrial revolution. At the same time, facing severe challenges in advanced manufacturing and safety assessment of high-performance materials and structures, classical computational mechanics has more and more limitations in achieving the goal of ‘half-time and half-cost’ of R&D cycle. Data-driven computational mechanics emerged in this context and showed great vitality. This paper aims to discuss and analyze the trend of data-driven computational mechanics by reviewing recent research achievements. In this paper, the algorithms in the framework of data-driven computational mechanics are summarized into two categories: the first one is based on energy functional, the key point of which is to construct the constitutive relationship by using material data; while the second category is based on distance functional, the specificity of which lies in directly embedding the material data into mechanical simulations. Several related data-driven algorithms of each category are briefly recalled, and the challenges and prospects of data-driven computational mechanics are discussed.
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Received: 27 December 2019
Published: 14 April 2020
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2020, Vol. 41 
