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physics.flu-dyn2025
A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems
Luca Menicali, Andrew Grace, David H. Richter +1
Fluid thermodynamics underpins atmospheric dynamics, climate science, industrial applications, and energy systems. However, direct numerical simulations (DNS) of such systems can b…
physics.flu-dyn2023
Physics-Informed Priors with Application to Boundary Layer Velocity
Luca Menicali, David H. Richter, Stefano Castruccio
One of the most popular recent areas of machine learning predicates the use of neural networks augmented by information about the underlying process in the form of Partial Differen…
physics.flu-dyn2023
A Physics-Informed, Deep Double Reservoir Network for Forecasting Boundary Layer Velocity
Matthew Bonas, David H. Richter, Stefano Castruccio
When a fluid flows over a solid surface, it creates a thin boundary layer where the flow velocity is influenced by the surface through viscosity, and can transition from laminar to…