Curriculum Vitae
Partha Pratim Das, Ph.D. • [email protected] • (817) 272-5783 • LinkedIn
Ph.D. in Mechanical Engineering specializing in advanced aerospace materials, multiphysics FEA (COMSOL, Abaqus), and physics-informed machine learning (PyTorch, NVIDIA Modulus) for structural health monitoring and materials durability. Built novel experimental dielectric techniques and PINN models for diffusion/electromagnetics in composite systems; recognized by ASC, PHM Society, and SAMPE. Currently Research Scientist II at UTA Research Institute contributing to AFRL AMPPAC materials characterization and performance modeling.
Education
Ph.D. in Mechanical Engineering
The University of Texas at Arlington, TX, US
Dissertation: A Multimodal Prediction Framework for Moisture Aging Assessment in Polymer Matrix Composites
Advisor: Dr. Rassel Raihan • Institute for Predictive Performance Methodologies (IPPM)
Finite Element Methods, Machine Learning, Structural Dynamics, Analytical Methods in Engineering, Polymer Science and Engineering, Analysis of Materials, Advanced Metal Additive Manufacturing, Embedded Microcontroller Systems.
B.Sc. in Mechanical Engineering
Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh
Undergraduate Thesis: Heat Transfer Performance of Internally Finned Tubes Using Water and Air
Technical Skills
Modeling & Machine Learning
COMSOL Multiphysics, Abaqus FEA, SolidWorks, Physics-Informed Neural Networks (PINNs), Neural Operators, NVIDIA Modulus / PhysicsNemo, PyTorch
Programming & Scientific Computing
Python (5+ years: PyTorch, NumPy, SciPy, Pandas, Scikit-Learn), MATLAB, C/C++ (Embedded systems)
Materials & Experimental Testing
Manufacturing & Prototyping
Polymer Composites (Compression Molding, Out-of-Autoclave [OOA], VARTM), CNC Machining, Additive Manufacturing (3D Prototyping)
Professional & Academic Experience
Research Scientist II
Institute for Predictive Performance Methodologies, The University of Texas at Arlington Research Institute (UTARI)
- Conducting active defense research under the US Air Force Research Laboratory (AFRL) Aerospace Materials Processing, Performance and Characterization (AMPPAC) program.
- Directing experimental mechanics, broadband dielectric spectroscopy (BbDS), and multiphysics modeling of composite airframes.
- Authoring technical progress reports and high-impact peer-reviewed publications; mentoring graduate research assistants.
Graduate Research Assistant
Institute for Predictive Performance Methodologies, The University of Texas at Arlington Research Institute (UTARI)
- Built a PINN framework (PyTorch, NVIDIA Modulus) for coupled diffusion–electromagnetics in FRP composites; benchmarked vs. COMSOL/Abaqus and dielectric data to validate field predictions and reduce simulation turnaround time.
- Ran multiphysics studies (Abaqus, COMSOL) coupling diffusion, electrical, and mechanical fields; delivered parameter sensitivity analyses to guide processing and maintenance decisions.
- Published peer-reviewed papers on composites, SHM, and multiphysics modeling across leading international journals.
Graduate Teaching Assistant
Mechanical and Aerospace Engineering, The University of Texas at Arlington
- Courses taught: Experimental Methods and Measurements, Solid Mechanics, Engineering Statics.
- Prepared problem-solving lectures, mentored undergraduate students in laboratory experiments, grading assignments, and proctoring examinations.
Awards & Honors
Certifications
Neural Networks and Deep Learning
DeepLearning.ai via Coursera
Comprehensive specialization covering foundational neural network architectures, vectorization, backpropagation, and deep learning implementations.