13 citations · 13 across the 5 of their papers we have counts for
6 papers
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…
Improving the stability and efficiency of high-order operator-splitting methods
Siqi Wei, Victoria Guenter, Raymond J. Spiteri
Operator-splitting methods are widely used to solve differential equations, especially those that arise from multi-scale or multi-physics models, because a monolithic (single-metho…
pythOS: A Python library for solving IVPs by operator splitting
Victoria Guenter, Siqi Wei, Raymond J. Spiteri
Operator-splitting methods are widespread in the numerical solution of differential equations, especially the initial-value problems in ordinary differential equations that arise f…
A pair of Second-order complex-valued, N-split operator-splitting methods
Raymond J. Spiteri, Siqi Wei
The use of operator-splitting methods to solve differential equations is widespread, but the methods are generally only defined for a given number of operators, most commonly two.…
Beyond Strang: A practical assessment of some second-order 3-splitting methods
Raymond J. Spiteri, Arash Tavassoli, Siqi Wei +1
Operator splitting is a popular divide-and-conquer strategy for solving differential equations. Typically, the right-hand side of the differential equation is split into a number o…
Fractional-Step Runge--Kutta Methods: Representation and Linear Stability Analysis
Raymond J. Spiteri, Siqi Wei
Fractional-step methods are a popular and powerful divide-and-conquer approach for the numerical solution of differential equations. When the integrators of the fractional steps ar…