A finite-element variational inference method delivers full-covariance Bayesian field reconstruction at dimensions exceeding 400,000 for 3D porous media flow using sparse precision parameterization from SPDE priors.
hipPYlib: An extensible software framework for large-scale inverse problems governed by PDEs: Part I: Deterministic inversion and linearized Bayesian inference
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Stochastic material heterogeneity modeled with Gaussian random fields in a nonlocal framework fundamentally changes phase nucleation, localization, and macroscopic mechanical response in architected metamaterials.
The work formulates a sparsity-promoting inverse problem for source identification in tomographic sensing of chemical plumes using level-set representations of concentration thresholds.
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Sparsity-Driven Source Localization in Tomographic Sensing Applications
The work formulates a sparsity-promoting inverse problem for source identification in tomographic sensing of chemical plumes using level-set representations of concentration thresholds.