RLNS regularizes LNS to perform block Gibbs sampling under entropy, interpolating between pseudolikelihood and exact MLE for differentiable combinatorial optimization.
The elements of differentiable program- ming
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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.
A feedback optimization pipeline for tri-level mobility games outperforms Bayesian optimization and genetic algorithms on Zurich multimodal data while identifying incentives that boost multimodal use.
This perspective paper categorizes hybrid architectures for combining mechanistic and data-driven models using residual learning, Neural ODEs, and solver-in-the-loop to model neurological disorder progression.
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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.