DR. Abdul Qadir Ibrahim
Computational Imaging
Researcher focused on neural operator theory, spectral methods, and sparsity-driven representations. My current work centers on adaptive operator learning over mixed Sobolev–Besov spaces, leveraging spectral basis dictionaries and gating mechanisms to construct efficient neural approximations. I explore the intersection of functional analysis and deep learning, targeting compact, data-driven approximations of nonlinear maps via sparse convex combinations of domain-specific bases.
Publications
Parallel-in-time methods with machine learning based coarse propagators
Ibrahim A - TUHH Universitätsbibliothek - 2026
Space-Time Parallel Scaling of Parareal with a Physics-Informed Fourier Neural Operator Coarse Propagator Applied to the Black-Scholes Equation
Ibrahim A, Götschel S, Ruprecht D - Proceedings of the Platform for Advanced Scientific Computing Conference - 2025
Space-time parallel scaling of Parareal with a physics-informed Fourier Neural Operator coarse propagator applied to the Black-Scholes equation
Ibrahim A, Götschel S, Ruprecht D - arXiv - 2024
Parareal with a Physics-Informed Neural Network as Coarse Propagator
Ibrahim A, Götschel S, Ruprecht D - Lecture Notes in Computer Science - 2023
Parareal with a physics-informed neural network as coarse propagator
Ibrahim A, Götschel S, Ruprecht D - arXiv - 2023
Functional hypoxia drives neuroplasticity and neurogenesis via brain erythropoietin
Wakhloo D, Scharkowski F, Curto Y, Javed Butt U, Bansal V, Steixner-Kumar A, Wüstefeld L, Rajput A, Arinrad S, Zillmann M, Seelbach A, Hassouna I, Schneider K, Qadir Ibrahim A, Werner H, Martens H, Miskowiak K, Wojcik S, Bonn S, Nacher J, Nave K, Ehrenreich H - Nature Communications - 2020
