Advancements in materials science and biomedical engineering increasingly rely on computational modeling to explore and optimize complex systems. My computational research spans molecular modeling and simulations, finite element analysis (FEA), deep learning, and image and signal processing, providing insights into material behavior, energy harvesting mechanisms, and biomedical applications.

Using state-of-the-art software such as LAMMPS, MATLAB, Python, COMSOL Multiphysics, BIOVIA Discovery Studio, ChimeraX, PyRx, and OVITO, I conduct simulations and analyses on metals, alloys, polymers, ceramics, composites, biomolecules, 2D materials, and energy harvesting systems. Additionally, my work in computer vision and deep learning focuses on feature extraction, segmentation, and motion tracking for biomedical applications.

Below are computational models and simulations that illustrate key aspects of my research.

Peridynamic simulations offer a powerful approach to modeling material deformation and failure, particularly in scenarios involving fracture mechanics, impact analysis, and dynamic loading. Unlike traditional finite element methods (FEM), peridynamics is a non-local continuum mechanics theory that allows for the natural emergence of cracks without requiring predefined crack paths.

In my research, peridynamic simulations are applied to impact testing and structural failure analysis, providing insights into how materials respond to extreme mechanical stress. This method is especially useful for studying brittle materials, composites, and high-entropy alloys, where complex fracture patterns and multiple crack propagations occur. By leveraging peridynamic modeling, I aim to enhance material durability and optimize structural designs for engineering applications.

Deep learning architectures are at the core of modern artificial intelligence (AI) applications, enabling machines to learn complex patterns from data. These architectures, often based on neural networks, consist of multiple layers that extract and refine features from raw inputs, making them highly effective for image processing, pattern recognition, and signal analysis.

In my research, I utilize deep learning models for vein feature extraction, motion tracking, and image segmentation. By leveraging techniques such as image segmentation, feature extraction, and convolutional neural networks (CNNs), I develop AI-driven solutions for applications like blood flow analysis and real-time health monitoring. These architectures play a crucial role in improving diagnostic accuracy, automation, and computational efficiency in biomedical engineering and materials science.