Neural network surrogate uses area fraction, shape descriptor τ, two-point correlation S2(r), and lineal-path function ℓ(z) to predict effective properties of hyperelastic Boolean microstructures, with added descriptors improving pointwise accuracy but not guaranteeing physical admissibility.
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Machine Learning Surrogate Modeling for Homogenization of Hyperelastic Materials with Boolean Microstructures
Neural network surrogate uses area fraction, shape descriptor τ, two-point correlation S2(r), and lineal-path function ℓ(z) to predict effective properties of hyperelastic Boolean microstructures, with added descriptors improving pointwise accuracy but not guaranteeing physical admissibility.