Biography
My name is Avinash Potluri, and I am pursuing my Master's in Mechanical Engineering from Texas A&M University–Kingsville. Having over five years of work experience as a Design Engineer and a Quality Analyst, I have very good quality control, process improvement, and design for manufacturing skills. I am skilled in software like SolidWorks, AutoCAD, CATIA, Ansys, SAP MM, and Power BI, and my experience is in supply chain management, lean practices, and project management. My project and academic work, ranging from enhancing the efficiency of heat transfer in a Wagon-RI(car) radiator to conceptualizing a fuel tank for a Formula SAE vehicle, demonstrate my focus on using engineering concepts to address real-world problems. In addition to technical knowledge, I possess a well-balanced perspective developed through teamwork, leadership, and social work. I have collaborated with overseas suppliers, participated in international student competitions, and engaged in volunteer activities with organizations such as NGOs focused on animal welfare and student mentorship programs. Through such activities, my people skills, adaptability, and commitment to working for a greater cause have been amplified. Aside from academics and professional work, I enjoy travel, soccer, teaching, gardening, and miniature painting. These activities challenge my creativity, work ethic, and curiosity.
Academic Information
Status: Masters Student
Year in Program: 2nd
Major/Specialty: Mechanical Engineering and Industrial Engineering
Degrees: 1- Diploma/ Mechanical Engineering/ 2012 to 2015, 2- Bachelor's/ Industrial and Production Engineering/ 2015 to 2018, 3- Master's/ Mechanical Engineering/ 2024 to 2026.
Research Areas
Applied Mathematics; Engineering; Machine Learning/AI; Physics
Research Interests
My research areas include computational fluid dynamics (CFD), numerical partial differential equations, scientific computing with machine learning, digital twinning, and advanced manufacturing. My thesis work includes shock dynamics flows with explicit and implicit finite difference solutions, generating benchmark data for CFD and machine-learning-based solutions. Beyond my thesis, I contribute collaborative research on physics-informed neural networks (PINNs) and AI for scientific discovery. For example, I am a coauthor on work applying PINNs to hypersonic blunt-cone flow simulations to solve forward and inverse PDE problems and developing generative AI techniques for shock dynamics that bridge PDE-based datasets with large language models. I am also academically interested in education and mentoring. I am currently a Graduate teaching assistant for Engineering Graphics and Elementary Numerical Methods and Engineering Problem Solving, helping students build computational and problem-solving skills. My general goal is to create hybrid CFD–AI methods for scientific discovery while being an active participant in education in computational mechanics.
Topical Areas
Applied Mathematics; Mechanical Engineering
Relevant Coursework
I have completed graduate-level courses. They include Continuum Mechanics, Conduction and Convection Heat Transfer, and Engineering Analysis in Applied Mechanics, where I established strong foundations in partial differential equations, thermofluidic phenomena, and applied mathematical modeling. I also completed Advanced Dynamics, Advanced Fluid Mechanics, and Finite Element Methods in Engineering, where I gained direct experience in the use of software tools like MATLAB and Abaqus for simulation and analysis. Aside from this, I am also pursuing my thesis work in shock dynamics and PDE-based modeling using explicit and implicit finite difference solvers, and extending it in the machine learning direction using Numpy, JAX on HPC systems. To further improve my skills in Python, I also enrolled in a Python Developer course (NC Lab) to sharpen my programming skills in scientific computing.
Publications & Research Projects
1- Shock Dynamics Flows Dataset Creation using Explicit and Implicit Finite Difference Solvers and Finite-JAX.(American Physics society). 2- Numerical and Analytical Modeling of Heat Transfer in Current-Carrying Conductors using the Heat Equation implemented using Finite-JAX (American Society of Thermal and Fluid Engineers). 3- Partition of Unity Physics-Informed Neural Networks (POU-PINNs) for Darcy Flow.(American Physics society). 4- Scientific Discovery of Shock Dynamics via Generative Artificial Intelligence.(American Physics society). 5- Physics-Informed Neural Networks: Forward and Inverse Design Solutions for Hypersonic Blunt Cones. (American Physics society).