A spectral dictionary model that reconstructs images as linear combinations of learned sinusoidal bases with a Gaussian prior on coefficients is proposed, but the paper does not provide a workable algorithm or reproducible evidence.
Mixed variational formulations for structural topology optimization based on the phase-field approach
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abstract
We propose a variational principle combining a phase-field functional for structural topology optimization with a mixed (three-field) Hu-Washizu functional, then including directly in the formulation equilibrium, constitutive, and compatibility equations. The resulting mixed variational functional is then specialized to derive a classical topology optimization formulation (where the amount of material to be distributed is an \emph{a priori} assigned quantity acting as a global constraint for the problem) as well as a novel topology optimization formulation (where the amount of material to be distributed is minimized, hence with no pre imposed constraint for the problem). Both formulations are numerically solved by implementing a mixed finite element scheme, with the second approach avoiding the introduction of a global constraint, hence respecting the convenient local nature of the finite element discretization. Furthermore, within the proposed approach it is possible to obtain guidelines for settings proper values of phase-field-related simulation parameters and, thanks to the combined phase-field and Hu-Washizu rationale, a monolithic algorithm solution scheme can be easily adopted. An insightful and extensive numerical investigation results in a detailed convergence study and a discussion on the obtained final designs. The numerical results clearly highlight differences between the two formulations as well as advantages related to the monolithic solution strategy; numerical investigations address both two-dimensional and three-dimensional applications.
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Spectral Dictionary Learning for Generative Image Modeling
A spectral dictionary model that reconstructs images as linear combinations of learned sinusoidal bases with a Gaussian prior on coefficients is proposed, but the paper does not provide a workable algorithm or reproducible evidence.