<ul><li>A new framework has been proposed for identifying mechanical properties of heterogeneous materials without a closed-form constitutive equation.</li><li>The framework involves training a neural network with Fourier features to capture sharp gradients in displacement field data obtained from digital image correlation.</li><li>A physics-based data-driven method using ordinary neural differential equations (NODEs) is employed to discover constitutive equations, allowing for representation of arbitrary materials while satisfying constraints.</li><li>The framework includes a hyper-network that optimizes parameters to minimize a multi-objective loss function considering constraints in the theory of constitutive equations, showcasing robustness in identifying mechanical properties of heterogeneous materials with minimal assumptions.</li></ul>

Fully data-driven inverse hyperelasticity with hyper-network neural ODE fields

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