Experiment-informed finite-strain inverse design of spinodal metamaterials

This study presents a novel physics-enhanced machine learning (ML) and optimization framework tailored to address the challenges of designing intricate spinodal metamaterials with customized mechanical properties in scenarios where computational modeling is restricted, and experimental data is sparse. By utilizing sparse experimental data directly, our approach facilitates the inverse design of spinodal structures with precise finite-strain mechanical responses. Leveraging physics-based inductive biases to compensate for limited data availability, the framework sheds light on instability-induced pattern formation in periodic metamaterials, attributing it to nonconvex energetic potentials. Inspired by phase transformation modeling, the method integrates multiple partial input convex neural networks to create nonconvex potentials, effectively capturing complex nonlinear stress-strain behavior, even under extreme deformations.