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Always learning
:octocat:
Always learning
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shayansss/README.md

My PhD and MSc Research Data

  • EHML: Extended Hybrid Machine Learning: Implementation of several extensions, including physics-constrained data augmentation, on multi-fidelity surrogate modeling using TensorFlow and Abaqus.
  • PSA: Pre-Stress Algorithm: This is a unified optimizer for large-scale pre-stressing analysis in articular cartilage models using Abaqus Fortran subroutines and Python scripts.
  • HML: Hybrid Machine Learning: 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.
  • PMSE: Pointwise Mean Squared Error: Implementation of a simple pointwise metric for machine-learning-based surrogate modeling in Python using Keras and Abaqus.
  • BioUMAT: Abaqus Fortran subroutine for cartilage multiphasic modeling: 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 modifications was used in several publications.
  • You can also download the LaTeX source code of my PhD dissertation from this repository.

Other Data

I cannot share my other data due to the code privacy related to my other jobs!

Pinned Loading

  1. hml hml Public

    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.

    Jupyter Notebook 11 2

  2. pmse pmse Public

    Implementation of a new pointwise metric using Keras and Abaqus.

    Jupyter Notebook 8 1

  3. bioumat bioumat Public archive

    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…

    Fortran 4 3

  4. psa psa Public

    Large scale implementation of pre-stressing in a multiphasic cartilage model in Abaqus

    Python 3

  5. ehml ehml Public

    Implementation of several extensions, including physics-constrained data augmentation, on multi-fidelity surrogate modeling using TensorFlow and Abaqus.

    Jupyter Notebook 1

  6. python python Public

    Python for sientific computing and data sience (in progress)

    Jupyter Notebook