2019
DOI: 10.1002/nme.6280
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An assessment of numerical techniques to find energy‐minimizing microstructures associated with nonconvex potentials

Abstract: Summary Microstructural patterns emerge ubiquitously during phase transformations, deformation twinning, or crystal plasticity. Challenges are the prediction of such microstructural patterns and the resulting effective material behavior. Mathematically, the experimentally observed patterns are energy‐minimizing sequences produced by an underlying non‐(quasi)convex strain energy. Therefore, identifying the microstructure and effective response is linked to finding the quasiconvex, relaxed energy. Due to its non… Show more

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Cited by 23 publications
(12 citation statements)
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References 70 publications
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“…for spinodoids, ρ ∈ {0} ∪ [ρ min , 1] and θ 1 , θ 2 , θ 3 ∈ {0} ∪ [0, π/2]. Similar observations were reported and explained previously by, e.g., Shiye and Jiejiang (2016) and Kumar et al (2020b).…”
Section: Benchmark Ii: L-shaped Structuresupporting
confidence: 88%
“…for spinodoids, ρ ∈ {0} ∪ [ρ min , 1] and θ 1 , θ 2 , θ 3 ∈ {0} ∪ [0, π/2]. Similar observations were reported and explained previously by, e.g., Shiye and Jiejiang (2016) and Kumar et al (2020b).…”
Section: Benchmark Ii: L-shaped Structuresupporting
confidence: 88%
“…FFT-based computational techniques proved useful for studying the viscoelasticity of cement paste [310], mortar samples [311], cement [312] and concrete [313]. Furthermore, FFT-based techniques were used for studying explosive materials [314], secondary creep in a porous nuclear fuel [315], the thermal expansion of an energetic material [316], optical properties of deposit models for paint [317], dynamic recrystallization [318], to compute geodesics in two-dimensional media [182], fitting microstructure-property relationships [319], topology optimization [320] and for finding emerging microstructures associated to non-convex potentials [93,321].…”
Section: Miscellaneousmentioning
confidence: 99%
“…One may conjecture that minimizing W + magic in a finite element space fails to find microstructure because each finite element coefficient influences only a very local part of the deformation function. Additionally, when the strain energy density lacks quasiconvexity (which we consider possible for W + magic ), FEM-methods generally fail [44] due to the non-uniqueness of the solution, i.e. the microstructures.…”
Section: Deep Neural Networkmentioning
confidence: 99%