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SinaNavidii/README.md

Hi there, I'm Sina!

I like climbing, coding, and coffee!🧗🏻‍♂️💻☕

I'm a Ph.D. student and graduate research assistant in the school of mechanical, aerospace and manufacturing engineering at UConn. My research is focused on the prognostics and health management (PHM) of complex engineering systems like lithium-ion batteries through data-driven and physics-based modeling and ultimately combining both in the form of physics-informed machine learning.

My journey into this field began with a robotics competition during my undergrad, where I got interested in programming and problem-solving. In my master’s program, I became drawn to operations research and mathematical modeling. Discovering gradient descent eventually led me to machine learning, and now I’m excited to be diving deeper into it.

These days, I’m hands-on with Python, MATLAB, and a range of data science and simulation frameworks to tackle problems and optimize solutions. Outside of research, you’ll find me climbing rocks, exploring coffee spots, or catching up on the latest tech podcasts!

📫 Reach out at sina.navidi@uconn.edu for collaborations, questions, or just to connect!

Connect with me:

Sina Navidi LinkedIn

Tools:

git matlab pandas python pytorch scikit_learn seaborn tensorflow comsol

My Publications:

Check out my latest work on Google Scholar.

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  1. REIL-UConn/Forecasting-Battery-Capacity-for-Second-Life-Applications-Using-PI-RNN REIL-UConn/Forecasting-Battery-Capacity-for-Second-Life-Applications-Using-PI-RNN Public

    The code repository for the paper "Forecasting Battery Capacity for Second-Life Applications Using Physics-Informed Recurrent Neural Networks"

    Python 3

  2. Model-Based-GPR Model-Based-GPR Public

    This repository provides an online model-based Gaussian process regression (GPR) framework for forecasting the capacity fade of lithium-ion batteries with different chemistries.

    Jupyter Notebook

  3. PIML-for-Degradation-Diagnostics PIML-for-Degradation-Diagnostics Public

    Main codes for half-cell model, PINN and co-kriging implemented for physics-informed degradation diagnostics project: https://doi.org/10.1016/j.ensm.2024.103343

    Python 2 2