Multiscale modeling of inelastic materials with Thermodynamics-based Artificial Neural Networks (TANN)
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Updated
Aug 26, 2024 - Python
Multiscale modeling of inelastic materials with Thermodynamics-based Artificial Neural Networks (TANN)
ANSYS Workbench extension for multiscale modeling of composite materials using SwiftComp. Provides seamless integration of homogenization, dehomogenization, and failure analysis. Features intuitive GUI for beam, plate, and solid models with built-in visualization tools.
3D full-field mechanical homogenization fenics code: finite strains and mixed stress controlled
3D full-field magnetomechanical homogenization fenics code: finite strains, viscosity, incompressibility and mixed stress/flux controlled
BCC unit cell with structured mesh in ABAQUS
Periodic Mesh Generation of BCC Lattice Materials using SALOME for FEM Analysis
Bauer, J. K., & Böhlke, T. (2022). On the dependence of orientation averaging mean field homogenization on planar fourth-order fiber orientation tensors. Mechanics of Materials, 104307.
Surrogates for microstructure property linkages that inherently fulfill the Voigt-Reuss bounds.
A Deep Learning Model For Homogenization of Two-Phase High-Contrast Three-dimensional Materials
Fiber reinforced composite analysis
Fenicsx Python FEM Project to compute cell homogenization and effective Stiffness Matrix
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