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sno

Spectral Neural Operator

sts-cnn

Q. Zhang, Q. Yuan, C. Zeng, X. Li, and Y. Wei, “Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network,” IEEE TGRS, 2018.

supreme

Super-Resolution of Multispectral Multiresolution Images from a Single Sensor

sve-r

A coupled Saint Venant Equations (SVE)- Richards Equation solver

swain-rainfallrunoff

Semi-distributed Rainfall-Runoff model, using Graph Neural Networks to model an entire watershed with around 500 catchments

temperature-downscaling

Source code for "Generating 1 km spatially seamless and temporally continuous air temperature based on deep learning over Yangtze River Ba-sin, China"

triplecollocation

post

tshydro

R package that estimates water level time series from satellite altimetry data

tsriver

tutorials_twd

Tutorials regarding towards data science

two-branch-cnn-multisource-rs-classification

ufno

U-FNO - an enhanced Fourier neural operator-based deep-learning model for multiphase flow

uqpce

Uncertainty Quantification using Polynomial Chaos Expansion (UQPCE) is an open source, python based research code for use in parametric, non-deterministic computational studies. UQPCE utilizes a non-intrusive polynomial chaos expansion surrogate modeling technique to efficiently estimate uncertainties for computational analyses. The software allows the user to perform an automated uncertainty analysis for any given computational code without requiring modification to the source. UQPCE estimates sensitivities, confidence intervals, and other model statistics, which can be useful in the conceptual design and analysis of flight vehicles. This software was developed for the Aeronautics Systems Analysis Branch (ASAB) within the Systems Analysis and Concepts Directorate (SACD) at NASA Langley Research Center to study potential impacts of uncertainties on the prediction of ground noise generated from commercial supersonic aircraft concepts.

uqpinns

validation_good_practice

Source code for the soil moisture validation good practice paper

water_gee

This repository provides a way to extract water bodies using deep learning methods in GEE.

weatherbench

A benchmark dataset for data-driven weather forecasting

wno

xai_fidelity_assessment_cnn_geo

xpinns

when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/10.1016/j.cma.2022.115346

ypea110-shuffled-complex-evolution

Shuffled Complex Evolution (SCE-UA) in MATLAB