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Always learning
:octocat:
Always learning
11 followers · 9 following

Achievements

Achievement: Starstruck

Achievements

Achievement: Starstruck

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shayansss/README.md

PhD and MSc Research Projects

Below is a selection of repositories related to my doctoral and master’s research work:

  • EHML: Extended Hybrid Machine Learning. Implementation of advanced extensions to hybrid machine learning frameworks, including physics-constrained data augmentation for multi-fidelity surrogate modeling. Developed using TensorFlow and integrated with Abaqus finite element simulations. Repository: https://github.com/shayansss/ehml

  • PSA: Pre-Stress Algorithm. A unified optimization framework for large-scale pre-stressing analysis in articular cartilage models. Implemented using Abaqus Fortran subroutines combined with Python-based automation scripts. Repository: https://github.com/shayansss/psa

  • HML: Hybrid Machine Learning. Implementation of a novel hybrid machine learning methodology for multi-fidelity surrogate modeling of finite element simulations, with applications in multi-physics modeling of soft biological tissues. Repository: https://github.com/shayansss/hml

  • PMSE: Pointwise Mean Squared Error. Development of a pointwise error metric tailored for machine-learning-based surrogate modeling. Implemented in Python using Keras and coupled with Abaqus simulations. Repository: https://github.com/shayansss/pmse

  • BioUMAT: Abaqus Fortran Subroutines for Cartilage Multiphase Modeling. Fortran 77 implementation of UMAT, FLOW, and SDVINI subroutines for a multiphasic cartilage model originally proposed in my Master’s thesis. With minor refinements, this model has been used in multiple peer-reviewed publications. Repository: https://github.com/shayansss/msc

  • PhD Dissertation. The full LaTeX source code of my doctoral dissertation is available here: Repository: https://github.com/shayansss/PhD

Other Data

Due to confidentiality agreements related to my professional engagements, I am unable to share other datasets and proprietary code developed in the context of industry projects.

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 16 2

  2. pmse pmse Public

    Implementation of a new pointwise metric using Keras and Abaqus.

    Jupyter Notebook 9 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 6 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