Tensor Toolbox for Modern Fortran

🔍 finite element analysis for continuum mechanics of solid bodies

Basics to implement user-defined materials (usrmat, umat) in LS-Dyna with Fortran

My Abaqus related stuffs (under development)

UMAT4COMSOL: An Abaqus user material (UMAT) subroutine wrapper for COMSOL

UMAT subroutines (for FEA code Abaqus) to model progressive damage/failure in FRP composites

User Material Model Implementation (UMAT) based on Finite Viscoelasticity Theory for Hydrogels in Abaqus

This is a nonlinear elastic constitutive model(Duncan-CHANG EB) UMAT widely used in the field of geotechnical engineering. It can be used in abaqus6.14-5 to calculate the dam settlement deformation.

UMAT for ABAQUS using 3D Hashin Based Orthotropic Damage Model

Implementation of a new hybrid machine learning technique for multi-fidelity surrogates of finite elements models with applications in multi-physics modeling of soft tissues.

A modern Fortran library for tensor calculus

A Tsai-Wu based 2D Damage Model UMAT for ABAQUS (discontinued)

This repository contains scripts for the batch generation of polycrystalline RVE models and finite element analysis operations. These scripts are used to simulate the generation of fatigue cracks in polycrystalline materials. The involved software includes Neper, Gmsh, and Abaqus 2022.

UMAT for ABAQUS using Mazars Concrete Damage Model with Fracture Energy Regularization

Comparison of Abaqus Library, UMAT, and Analytical Solution for Neo-Hookean Hyperelastic Material Under Large Deformations

A Hashin 2D Damage Model UMAT for ABAQUS (discontinued)

This code is the Fortran 77 version of the UMAT, FLOW, and SDVINI subroutines of the cartilage model, I firstly proposed in my Master's thesis. The model with minor modification was used in several publications.

Fortran code for the transformation into the logarithmic strain space

Here you can verify if your OpenCV has OpenCL enabled using an implementation of the T-API and also compare the performance of Mat and UMat

This repository demonstrates multiscale modeling of copper heat pipes using machine learning, integrating grain-scale data with FEA via a UMAT. It highlights grain size’s impact on stress, strain, and heat transfer for optimized material design.