dart is a simple dart throwing simulator with random inaccuracies used for prototyping data assimilation and inverse problems
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Updated
Aug 27, 2023 - Python
dart is a simple dart throwing simulator with random inaccuracies used for prototyping data assimilation and inverse problems
An adjointable cardiac mechanics data assimilator.
A case study of Stommel two box-model of AMOC using ETKF Data Assimilation with DAPPER.
Python API for Numerical Weather Prediction (NWP) and Data Assimilation Applications
Source code and data for the paper "Data Assimilation in Large Prandtl Rayleigh-Bénard Convection from Thermal Measurements" by A. Farhat, N. E. Glatt-Holtz, V. R. Martinez, S. A. McQuarrie, and J. P. Whitehead.
Data Assimilation with ML/DL methods
ADAO - SALOME module for Data Assimilation and Optimization
5 types of Kalman Filters and examples.
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.
Nonlinear, sub-pixel correction for geophysical interpolation
A library for both Simulation and INference of dynamical systems. Maintained by @alcrene
Nino-Ruiz, Elias D., and Sebastian Racedo Valbuena. "TEDA: A Computational Toolbox for Teaching Ensemble Based Data Assimilation." Computational Science–ICCS 2022: 22nd International Conference, London, UK, June 21–23, 2022, Proceedings, Part IV. Cham: Springer International Publishing, 2022.
DAHSI code package for model selection with hidden variables
Cryospheric Monitoring and Prediction Online
This is a basic python interface to CRTM v2.3.0.
A Python-based Blended sEamLess soLver for Atmospheric dynamics coupled to an ensemble data assimilation engine
Pre- and post-processing for MIKE FM Data Assimilation
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.
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