GeoHydrodynamics and Environment Research
-
Updated
Dec 18, 2023 - JavaScript
GeoHydrodynamics and Environment Research
FEniCS implementation of the numerical method introduced in the paper E. Burman, M. Nechita and L. Oksanen, Unique continuation for the Helmholtz equation using stabilized finite element methods, J. Math. Pures Appl., 2019.
sequential MCMC method for data assimilation
A simulated experiment to test novel applications of ensemble filtering methods to adjust for misreported time in weather prediction.
An adjointable cardiac mechanics data assimilator.
Notebooks to test out ideas or develop understanding
This is a collection of scripts and functions that serve as exercises for understanding and writing various numerical methods from scratch.
Data Assimilation Project with the Lorenz63 Model
This repository is the reproducible code of the paper Data Assimilation using ERA5, ASOS, and the U-STN model for Weather Forecasting over the UK. This paper has been accepted in the NeurIPS 2023 Workshop: Tackling Climate Change with Machine Learning.
Python tools and latex files for the Colloquium
Experiments for online learning and data assimilation for time series data.
A Python-based Blended sEamLess soLver for Atmospheric dynamics coupled to an ensemble data assimilation engine
A project to correct misreported observation times in data assimilation
An interactive jupyter notebook to illustrate Kalman-Bucy filter.
Correlation functions versus field-level inference in cosmology: example with log-normal fields
Nonlinear, sub-pixel correction for geophysical interpolation
A set of Data assimilation tools. Filter smoothers, ensemble and variational methods, localization, etc.
Lorenz 63 and Lorenz 96 reconstruction state with data assimilation and neural networks.
An AOT-based algorithm to estimate multiple unknown parameters in the Kuramoto-Saviashinski equation. Source code for the paper "Concurrent Multiparameter Learning Demonstrated on the Kuramoto-Sivashinsky Equation" by Pachev, Whitehead, and McQuarrie.
Add a description, image, and links to the data-assimilation topic page so that developers can more easily learn about it.
To associate your repository with the data-assimilation topic, visit your repo's landing page and select "manage topics."