6 papers
High-order Simulation of Nearly Hydrostatic Atmospheric Flows
Shoyon Panday, Stéphane Gaudreault, Carlos Pereira +1
Accurate simulation of atmospheric flows is challenging. Low-Mach-number dynamics often need to be resolved in the presence of a dominant, nearly hydrostatic background state. Conv…
Thermodynamically Constrained Information Geometric Regularization for Compressible Flows
Seth Taylor, Raymond J. Spiteri, Stéphane Gaudreault
We construct and analyze a thermodynamic extension of the recently proposed information geometric regularization of Cao and Schäfer. The construction extends their shock-mitigating…
Learning to Advect: A Neural Semi-Lagrangian Architecture for Weather Forecasting
Carlos A. Pereira, Stéphane Gaudreault, Valentin Dallerit +9
Machine-learning approaches to weather forecasting often employ a monolithic architecture in which distinct physical mechanisms, such as advection, diffusive mixing, thermodynamic…
Characteristic Bending in Incompressible Flows
Matthew Blomquist, Stéphane Gaudreault, Maxime Theillard
We present the Characteristic Bending (CB) method, a general framework for advecting quantities under incompressible velocity fields. The method builds on standard semi-Lagrangian…
Fast and Flexible Probabilistic Forecasting of Dynamical Systems using Flow Matching and Physical Perturbation
Siddharth Rout, Eldad Haber, Stephane Gaudreault
Learning dynamical systems from incomplete or noisy data is inherently ill-posed, as a single observation may correspond to multiple plausible futures. While physics-based ensemble…
Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data
Siddharth Rout, Eldad Haber, Stéphane Gaudreault
The modeling of dynamical systems is essential in many fields, but applying machine learning techniques is often challenging due to incomplete or noisy data. This study introduces…