- 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.
Always learning
Python (AI and data science) | Fortran (numerical analysis)
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CAEmate S.R.L.
- Bozen-Bolzano, Italy
- shayansss.github.io
- in/shayansajjadi
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