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This study introduces a hybrid physics-data surrogate model conditioned on microstructural variables to optimize the mechanical behavior of mycelium-woodchip composites through efficient multiscale simulations. By leveraging a hypernetwork, the model achieves accurate predictions even with limited training data, allowing for the optimization of functionally graded structures. The key result shows a 42% reduction in peak stress for a graded multiscale disk compared to one with a random microstructure, demonstrating the practical utility of this approach in sustainable material design.
Conditioning surrogate models on microstructural variables can reduce peak stress in engineered materials by over 40%, revolutionizing multiscale optimization.
Emerging sustainable materials increasingly rely on engineered hierarchy and microstructure to achieve control of their properties and mechanical behavior. Optimizing these materials with controllable microstructures requires efficient multiscale simulations. Data-driven surrogate models for the microscale can accelerate multiscale simulations, but require large amounts of data even for a fixed microstructure. When a range of microstructures is considered, as is the case in multiscale optimization, even more data is needed to train a surrogate. To overcome this challenge, we condition a hybrid physics-data surrogate on microstructural variables using a hypernetwork. This approach enables accurate predictions of multiscale mechanical behavior for a mycelium-woodchip composite material, even when trained on small datasets. The conditioned surrogate makes multiscale simulations of functionally graded structures tractable, and we validate it against a full FE^2 simulation. We optimize a graded multiscale disk, and reduce the peak stress by 42% compared to one with a random microstructure. Then, we go one step further, conditioning the network directly on manufacturing variables that can have a complex influence on the microstructure. This is a practical route to engineer the microscale for desired macroscale behavior. This contribution highlights the benefits of microarchitectured structures and demonstrates how conditioned surrogate models enable their multiscale optimization, which will accelerate the development and design of future sustainable materials and structures.
