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physics.flu-dyn2026
Well-posed by Design: Learning Constitutive Laws from Velocity Data using Convex Neural Network Potentials
Gonzalo G. de Diego, Georg Stadler
Learning constitutive laws of complex fluids from velocity data (i.e. indirect observations) is a PDE-constrained inverse problem in which expressive neural parameterizations risk…
physics.flu-dyn2025
Non-Newtonian viscous fluid models with learned rheology accurately reproduce Lagrangian sea ice simulations
Gonzalo G. de Diego, Georg Stadler
Polar sea ice is crucial to Earth's climate system. Its dynamics also affect coastal communities, wildlife, and global shipping. Sea ice is typically modeled as a continuum fluid u…
physics.flu-dyn2024
Modeling sea ice in the marginal ice zone as a dense granular flow with rheology inferred from discrete element model data
Gonzalo G. de Diego, Mukund Gupta, Skylar A. Gering +2
The marginal ice zone (MIZ) represents the periphery of the sea ice cover. In this region, the macroscale behavior of the sea ice results from collisions and enduring contact betwe…