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bbahtiri/README.md

Hi there, I'm Betim (BBahtiri) 👋

Dr.-Ing. | Computational Mechanics | AI in Engineering Science | PINNs, FEM, Data Science & Computer Vision 🚀
Passionate about developing and applying advanced computational methods and AI to solve complex engineering and scientific challenges.

LinkedIn Google Scholar

🛠️ My Tech Stack & Expertise

Languages & Scripting:
Python C++ Fortran MATLAB 		Machine Learning & AI:
TensorFlow PyTorch scikit-learn Keras Pandas NumPy OpenCV
Computational Science & Simulation:
FEM deal.II Abaqus PINNs UEL/UMAT Subroutines 		Tools & Platforms:
Linux Git GitHub Docker HPC LaTeX

🚀 Highlighted Projects Details for each project can be expanded by clicking on them.

1. Hybrid ML-FEM Viscoelastic-Viscoplastic Damage Model (Published in CMAE)
  • Objective: Implemented a sophisticated finite element model combining traditional constitutive laws with LSTM neural networks to simulate complex material behavior in epoxy nanocomposites under cyclic loading, considering moisture and nanoparticle effects.
  • Tech Stack: C++, deal.II (FEM Library), Python (for ML aspects), MPI, CMake.
  • Key Contributions: Developed a hybrid ML-FEM framework for large deformation solid mechanics, integrated LSTM for computational acceleration, modeled multi-network viscoelastic-viscoplasticity with damage, and incorporated environmental effects.
  • Outcome/Impact: Created a robust model for simulating advanced material responses, published in Computer Methods in Applied Mechanics and Engineering. Showcased seamless switching between physics-based and ML models.
  • DOI: 10.1016/j.cma.2023.116293
  • Repository: [Link to Project Repository]

  • Rheological Model

2. Deep Learning-Based Thermodynamically Consistent Material Model (Published in CMAE)
  • Objective: Proposed a physics-informed deep learning (DL) constitutive model for epoxy composites that enforces thermodynamic principles, using experimental data to predict material behavior under diverse ambient conditions (temperature, moisture, nanoparticle volume fraction).
  • Tech Stack: Python, Deep Learning (LSTM, Feed-Forward Neural Networks), Experimental Data Analysis.
  • Key Contributions: Designed a DL architecture combining LSTM and FFNNs to predict internal variables and free-energy, ensuring thermodynamic consistency. Trained solely on experimental data to capture complex, nonlinear, temperature- and moisture-dependent responses.
  • Outcome/Impact: Developed a novel DL model capable of accurately predicting material behavior while adhering to thermodynamic laws, published in Computer Methods in Applied Mechanics and Engineering.
  • DOI: 10.1016/j.cma.2024.117038
  • Repository: [Link to Project Repository]

  • Thermodynamic Consistent DL Model Architecture

3. GNN Force Field for Coarse-Grained Molecular Dynamics
  • Objective: To develop a deep learning model capable of predicting atomic forces in coarse-grained molecular dynamics (MD) simulations, creating a fast and accurate surrogate for traditional, computationally expensive force fields.
  • Tech Stack: Python, PyTorch, PyTorch Geometric, Pandas, NumPy, Scikit-learn.
  • Key Contributions: Implemented a Graph Neural Network (GNN) inspired by the state-of-the-art GNNFF architecture. Engineered a rotationally covariant force prediction module that learns scalar force magnitudes and projects them onto direction vectors, ensuring physical consistency. Developed a robust data pipeline including dynamic outlier filtering and Z-score normalization to handle noisy simulation data.
  • Outcome/Impact: Created a complete, end-to-end pipeline for training a GNN-based force field that can significantly accelerate MD simulations of polymer systems, bridging the gap between the speed of classical potentials and the accuracy of ab initio methods.
  • Repository: gnn-cg-peo-forcefield

  • GNN Force Field Parity Plot

4. Crack Detection in Electromechanical Materials using U-Net (Computer Vision)
  • Objective: Applied deep learning (U-Net with ResNet backbones) for semantic segmentation of crack propagation in materials under electromechanical stress, analyzing phase-field and electrical potential patterns from FEM simulations.
  • Tech Stack: Python, TensorFlow, Keras, OpenCV, ABAQUS (for data generation).
  • Key Contributions: Implemented a multi-class semantic segmentation pipeline for pixel-level crack detection, utilized transfer learning, and automated hyperparameter tuning. Compared phase-field and electrical potential visualization methods.
  • Outcome/Impact: Achieved high precision (IoU > 0.95) in detecting and classifying cracks/defects, offering a significant improvement over traditional methods.
  • Repository: Computer-Vision-Crack-Detection

  • Crack Detection Example

5. Predictive Maintenance System for Manufacturing using machine learning
  • Objective: Developed a machine learning system to predict 5 different types of equipment failures (TWF, HDF, PWF, OSF, No Failure) in manufacturing environments using sensor data, enabling proactive maintenance.
  • Tech Stack: Python, Scikit-learn, XGBoost, Pandas, Matplotlib, Seaborn.
  • Key Contributions: Performed comprehensive EDA, extensive feature engineering, implemented multi-class classification models, and created a configurable pipeline with advanced visualizations. Utilized the AI4I 2020 Predictive Maintenance Dataset.
  • Outcome/Impact: Built a system achieving strong predictive performance across various failure types, providing a practical solution for reducing downtime in industrial settings.
  • Repository: Predictive_Maintenance
6. ABAQUS Multiphysics Diffusion UEL
  • Objective: Implemented a User Element (UEL) for ABAQUS to simulate coupled hydro-mechanical behavior of moisture diffusion in polymer materials, capturing stress-assisted transport mechanisms.
  • Tech Stack: Fortran, ABAQUS (UEL Development), MATLAB (for visualization).
  • Key Contributions: Developed a 20-node quadratic hexahedral UEL for multiphysics coupling (stress-assisted diffusion), enabling monolithic solution of mechanical and diffusion fields. Provided MATLAB tools for post-processing.
  • Outcome/Impact: Created a flexible tool for advanced simulation of moisture diffusion in polymers under mechanical stress, applicable to aerospace, marine, and electronics industries.
  • Repository: ABAQUS-Multiphysics-Diffusion-UEL

📊 My GitHub Stats

BBahtiri's GitHub stats Top Languages

GitHub Streak

📈 Contribution Graph

BBahtiri's Contribution Graph

📫 How to Reach Me

  • Email: [email protected]
  • LinkedIn: Dr.-Ing. Betim Bahtiri
  • Feel free to open an issue on any of my repositories if you have questions or want to collaborate!
  • The projects are not related to my current employer !

Thanks for stopping by! Connect with me to explore collaborations and innovative ideas.

Pinned Loading

  1. Deep-Learning-Constitutive-Model Deep-Learning-Constitutive-Model Public

    A physics-informed deep learning (DL)-based constitutive model for investigating epoxy based composites under different ambient conditions.

    Python 16 4

  2. LSTM-Assisted-Viscoelastic-Viscoplastic-Model-FEM LSTM-Assisted-Viscoelastic-Viscoplastic-Model-FEM Public

    A finite element implementation of a viscoelastic viscoplastic constitutive model using the deal.ii library with the option to include a pretrained Deep-Learning model

    C++ 9 6

  3. Predictive-Maintenance-Project Predictive-Maintenance-Project Public

    Machine learning system for predicting equipment failures in manufacturing environments

    Python 2

  4. Computer-Vision-Crack-Detection Computer-Vision-Crack-Detection Public

    A deep learning pipeline for semantic segmentation of cracks or defects in materials using U-Net architecture with various backbones. This project includes hyperparameter tuning, custom model archi…

    Python 1

  5. Physics-Informed-Deep-Learning-Constitutive-Model Physics-Informed-Deep-Learning-Constitutive-Model Public

    An updated implementation for the thermodynamic consistent physics-informed deep learning model

    Python 1

  6. gnn-cg-peo-forcefield gnn-cg-peo-forcefield Public

    Graph Neural Network for Coarse-Grained Molecular Dynamics Force Fields.

    Python