Dr. Siddhant Agarwal

I am interested in the transformative potential of machine learning in fluid dynamics. From speeding up computationally demanding simulations to building sophisticated surrogate models that are fast and differentiable, machine learning promises to unlock previously inaccessible parameter spaces as well as scales in space and time. My current research interests revolve around data-efficiency, scalability and generalisability of such forward modelling methods in the form of physics-based algorithms and scientific foundation models.