Microstructure interpolation for macroscopic design

By Andrew D. Cramer, V. Challis, A. Roberts. 2016

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Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems (2020, background): “With the growth of data resources [18], a promising direction is to generate a database of the metamaterial microstructures and then utilize it to enable efficient data-driven design of a heterogeneous system [19-28].”
Compatibility in microstructural optimization for additive manufacturing (2019, methodology): “[14], decoupled multiscale optimization is performed by first generating a family of microstructures optimized for maximum bulk modulus under linearly-varying volume constraint, and subsequently fitting their properties to a functional which then replaces the SIMP model (Eq.”