Microstructures to control elasticity in 3D printing

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How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning (2021, background): “Metamaterials are traditionally designed through empirical trial-and-error or intuition [1] or computationally expensive topology optimization [31, 30, 3, 1, 29] which often produces designs out of predetermined geometrical building blocks.”
Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems (2020, background): “[29] proposed to construct a database with different metamaterial families, each containing similar microstructures and covering a respective region”
Globally Approximate Gaussian Processes for Big Data With Application to Data-Driven Metamaterials Design (2019, methodology): “Unlike previous data-driven works that represent unit cells as signed distance ﬁ elds [9] or voxels [11], we drastically reduce the input dimension in our dataset by characterizing the unit cells via spectral shape descriptors based on the Laplace – Beltrami (LB) operator.”
Infill Optimization for Additive Manufacturing—Approaching Bone-Like Porous Structures (2016, neutral): “The assembly of shapes from manufacturable micro-structures has been investigated in computer graphics [17], [18], [19] and topology optimization (e.”
Current and future trends in topology optimization for additive manufacturing (2018, methodology): “[102] Schumacher C, Bickel B, Rys J, Marschner S, Daraio C, Gross M.”