Welcome to My Homepage
Welcome to My Homepage
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I am Kabir Bakhshaei, a PhD student in Artificial Intelligence (AI) in a joint program between SSSA (Scuola Superiore Sant’Anna) and the University of Pisa, within the Italian National PhD Program in AI for Society. I am a part of the ERC DANTE project at SMARTLab, BioRobotics Institute, under the supervision of Prof. Giovanni Stabile.
My research lies at the intersection of AI and real-world physical systems. I focus on Scientific Machine Learning (SciML), physics-informed models, and large-scale simulation data to develop AI-driven surrogate models, reduced-order models, and data assimilation frameworks. My work targets applications such as urban microclimate prediction, environmental flows, engineering systems, and sustainability-oriented modelling.
Before starting my PhD, I spent three years as a researcher in the MathLab Group at SISSA (International School for Advanced Studies) with Prof. Gianluigi Rozza, working on data-driven and physics-based methods for cardiovascular flows, wind energy, and heat transfer.
I am passionate about applying AI, machine learning, and scientific computation to solve practical and measurable real-world problems, especially where physics, data, and sustainability intersect.
Highlights
Highlights
A Hybrid Discretize-then-Project Reduced Order Model for Turbulent Flows on Collocated Grids with Data-Driven Closure (ArXiv)
A Hybrid Discretize-then-Project Reduced Order Model for Turbulent Flows on Collocated Grids with Data-Driven Closure (ArXiv)
A Hybrid Discretize-then-Project Reduced Order Model for Turbulent Flows on Collocated Grids with Data-Driven Closure (ArXiv)
Integrating Transformers with Data Assimilation for Efficient Time Series Prediction in Inverse Heat Conduction Problems, ECCOMAS CONGRESS (Link)
Integrating Transformers with Data Assimilation for Efficient Time Series Prediction in Inverse Heat Conduction Problems, ECCOMAS CONGRESS (Link)
Integrating Transformers with Data Assimilation for Efficient Time Series Prediction in Inverse Heat Conduction Problems, ECCOMAS CONGRESS (Link)
High-Fidelity Wind Turbine Wake Prediction through CNN-based Superresolution Techniques, Scientific Machine Learning, Emerging Topics conference (SMLET24) (link)
High-Fidelity Wind Turbine Wake Prediction through CNN-based Superresolution Techniques, Scientific Machine Learning, Emerging Topics conference (SMLET24) (link)
High-Fidelity Wind Turbine Wake Prediction through CNN-based Superresolution Techniques, Scientific Machine Learning, Emerging Topics conference (SMLET24) (link)
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