Phys.org November 23, 2022
Combinatorial problems arising in puzzles, origami, and (meta) material design have rare sets of solutions, which define complex and sharply delineated boundaries in configuration space. The boundaries are difficult to capture with conventional statistical and numerical methods. Researchers in the Netherlands have shown that convolutional neural networks can learn to recognize these boundaries for combinatorial mechanical metamaterials, down to the finest detail, despite using heavily undersampled training sets, and can successfully generalize. According to the researchers even if machine learning is typically a “black box” approach, it can still be very valuable for exploring the design space for metamaterials, and potentially other materials, objects, or chemical substances. This could in turn potentially help to reason about and better understand the complex rules underlying effective designs…read more. TECHNICAL ARTICLE
Example of a six-step random walk-through design space… Credit: Phys. Rev. Lett. 129, 198003, 2 November 2022Â Â