Tempest
A computational physics laboratory for numerical methods, PDE simulation, and scientific machine learning.
ArchivedNumerical methods are fundamental to computational science, yet they are often encountered only as equations or black-box implementations. Tempest was created to build these methods from first principles, validate them against analytical solutions and reference implementations, and understand how they behave across diverse physical systems.
Rather than treating numerical algorithms as tools to apply, Tempest treats them as objects of study, explored through implementation, experimentation, validation, and comparison.
Numerical Methods
Finite difference methods, time integration schemes, stability analysis, and convergence studies.
Physical Systems
Partial differential equations ranging from linear advection and diffusion to shallow water dynamics and geophysical fluid models.
Scientific Machine Learning
Neural operators and physics-informed approaches alongside traditional numerical methods.
Geophysical Systems
Fourier Neural Operators
Stability Analysis of Numerical Methods
Explore Tempest.
The repository preserves an archived computational laboratory for numerical methods, PDE simulation, and scientific machine learning.