Transfer learning of deep material network for seamless structure–property predictions
Modern materials design requires reliable and consistent structure–property relationships. The paper addresses the need through transfer learning of deep material network (DMN). In the proposed learning strategy, we store the knowledge of a pre-trained network and reuse it to generate the initial st...
|Published in:||Computational Mechanics : Solids, Fluids, Structures, Fluid-Structure Interactions, Biomechanics, Micromechanics, Multiscale Mechanics, Materials, Constitutive Modeling, Nonlinear Mechanics, Aerodynamics, Vol. 64, No. 2 (2019), p. 451-465|
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