A framework learns constitutive priors from noisy data to enable PDE-constrained inverse design of elastic networks using latent variables, homotopy continuation, Chamfer distance matching, and neural smoothness constraints.
Invariant formulation of hyperelastic transverse isotropy based on polyconvex free energy functions.International journal of solids and structures, 40(2):401–445
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Constitutive Priors for Inverse Design
A framework learns constitutive priors from noisy data to enable PDE-constrained inverse design of elastic networks using latent variables, homotopy continuation, Chamfer distance matching, and neural smoothness constraints.