Gabriele Immordino, PhD
Aerospace Engineer & Machine Learning Researcher
I develop machine learning and AI methods to solve complex engineering problems, with a focus on computational aerodynamics, geometric deep learning, and physics-informed modelling. My goal is to bridge data-driven approaches with fundamental physical principles for robust, interpretable AI systems.
About Me
I am a researcher focused on applying machine learning and AI techniques to solve complex engineering problems. My work spans computational aerodynamics, geometric deep learning, and physics-informed machine learning models — leveraging high-resolution CFD data, graph neural networks, and nonlinear modelling to advance the field.
I hold a PhD from the University of Southampton on "Data-driven modelling of nonlinear aerodynamics in high-speed aircraft using machine learning" (2025). I have published in top-tier venues including AIAA Journal, Journal of Computational Physics, Aerospace Science and Technology, and Journal of Aircraft.
Research Highlights
ML for Engineering
Deep learning and statistical methods for aerodynamics prediction, structural analysis, and thermal modeling using high-resolution CFD data.
Learn more →Geometric Deep Learning
Graph convolutional networks and autoencoders for mesh-based, non-homogeneous unstructured engineering data.
Learn more →Physics-Informed AI
Integrating conservation laws and physical principles into ML models for physically consistent predictions.
Learn more →Large Language Models
Applying LLMs and retrieval-augmented generation (RAG) for intelligent document analysis and knowledge extraction in engineering contexts.
Learn more →Reinforcement Learning
Using RL for optimization and control in engineering systems, including multi-objective optimization and autonomous control.
Learn more →Explore
Projects
SKYSAFE, Draconian, Pilatus optimization, and PhD research on nonlinear aerodynamics with ML.
View Projects →Curriculum Vitae
Download my full CV in English or German with education, experience, and skills.
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