Dr. Vahid Reza Hosseini – Researcher
Office: 1225 (Building H1)
Phone: +49 40 6541-3831
Email: hosseinv.(at).hsu-hamburg.de
Vahid Reza is a computational scientist with expertise in numerical methods, scientific computing, and applied machine learning. He has hands-on experience with physics-informed neural networks (PINNs), surrogate modeling, and uncertainty quantification, applied to multi-physics problems in materials science, energy systems, and fluid-structure interaction. Proficient in Python, MATLAB, and high-performance computing, he has authored 20+ peer-reviewed publications and contributed to large-scale modeling and simulation projects across academia and industry.
Technical Skills:
- Programming Languages: Python, MATLAB, LaTeX, Fortran
- Python Packages: Pytorch, Jax, Pandas, NumPy, Seaborn, SciPy, Scikit-learn, TensorFlow 2, Bayesflow
- Commercial software: COMSOL Multiphysics, Avizo
Research interest
- Physics-Informed Machine Learning (PIML): DeepONets, Physics-Informed Neural Networks, Gaussian Processes, Fourier Neural Operator
- Simulation and modelling methods for batteries
- Solving inverse problems & parameter estimation
- Data-driven modeling in computational mechanics & materials science
- Peridynamic differential operators & nonlocal mechanics
- Meshless numerical methods for solid/fluid mechanics.
- Applications of Fractional Calculus in Science & Engineering
- Bio-Mathematics
Curriculum Vitae:
Since 2023:
Researcher
Institute of Applied Mathematics
Helmut Schmidt University Hamburg
Projects:
- dtec.bw: https://www.hsu-hh.de/am/dtec-bw
- Hibrain: https://www.hsu-hh.de/am/hibrain
Recent Publications:
Peer Review Activities:
- Neural computing & applications
- Applied soft computing
https://orcid.org/0000-0003-3427-2293
Letzte Änderung: 28. Mai 2025