Project Portfolio

Extending WeStatiX Cloud Software: Developing Digital Twins for Intelligent Structural Health Monitoring

• Duration: One year.
• Summary: A large-scale project at CAEmate focused on applying artificial intelligence and data science to digital twins and operational modal analysis for monitoring civil engineering structures. The developed commercial code is being gradually integrated into WeStatiX. The main objective is to translate multivariate sensor data into interpretable vibration analysis using statistical methods. This is followed by the application of unsupervised learning for anomaly detection and clustering, as well as supervised learning to align the data-driven model with physics-based modeling. Machine learning–accelerated optimization is then used to create digital twins of large structures. The key advantage of this framework is its fully automated platform, which requires near-zero tuning and enables straightforward 24/7 monitoring.
• Technologies: Python (PyTorch, Sklearn, Matplotlib, NumPy, Pandas, SciPy, and Paraview scripts) and Git (on Linux Shell).
• Fields: Data science (machine learning, signal processing, and time-series stats), applied mathematics (numerical analysis and optimization), and engineering physics (operational modal analysis).

PhD Dissertation: Hybrid Machine Learning and Numerical Analysis of Cartilage Biomechanics

• Duration: 4 years and 6 months.
• Summary: This research introduces data-efficient AI algorithms, such as hybrid graph neural networks, for simulating tissue biophysics, focusing on cartilage biomechanics. It also presents a novel approach to generating high-fidelity training data using advanced finite element methods and optimization algorithms. The proposed methods efficiently scale across different physical fidelities and scales. This dissertation is written in English, based on my published papers and code, under the supervision of Prof. Bruno Carpentieri and Prof. Gerhard A. Holzapfel.
• Technologies: Python (TensorFlow, Keras, Matplotlib, NumPy, Sklearn, Pandas, SciPy, and Abaqus scripts) and Fortran (Abaqus subroutines).
• Fields: Data science (machine learning), applied mathematics (numerical analysis and optimization), and engineering physics (biomechanics).

MSc Thesis: Computational and Biomechanical Investigation into the Degeneration of the Main Articular Cartilage Constituents in Osteoarthritis

• Duration: 2 years and 6 months.
• Summary: This research presents a novel and accurate computational model using finite element analysis to simulate osteoarthritis, i.e., the degeneration of joint substructures, particularly articular cartilage. The study highlights the critical role of subchondral bone changes in fluid movement through the joint, affecting both chemical and solid components. This thesis is primarily in Persian (nearly the same as my first journal paper in English), supervised by Prof. Mohammad Haghpanahi and Prof. Mohammad Razi.
• Technologies: Python (Abaqus scripts) and Fortran (Abaqus subroutines).
• Fields: Applied mathematics (numerical analysis) and engineering physics (biomechanics).