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.
Printing ferromagnetic domains for untethered fast-transforming soft materials.Nature, 558(7709):274–279
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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.