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Windows command line program for Spleeter, pure C, no Python. GUI: https://github.com/wudicgi/SpleeterGui | Spleeter 的 Windows 命令行程序,纯 C 语言编写,无需 Python.
Deezer source separation library including pretrained models.
Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods
🌦️ A catalogue and categorization of AI-based weather forecasting models.
DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
Earth System Modeling Framework cap for SCHISM
Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM)
An intake plugin for parsing an Earth System Model (ESM) catalog and loading assets into xarray datasets.
Hybrid ML + physics model of the Earth's atmosphere
A package for adding parametric uncertainty to the national structure inventory and estimating flood losses with uncertain depth-damage relationships
Smart Sea Level Sensors - Access pre-prepared water level sensor data
perturbation of coupled model input over a space of input variables
The official web site of the OGC SensorThings API standard specification.
Python interface for handling the SCHISM model.
This repository includes functions/classes/scripts for daily routine work. A part of it is about pre/post-processing schism model inputs/outputs
Matlab tools for loading, interpolating, and displaying BedMachine ice sheet topography.
A Complete Server implementation of the OGC SensorThings API
A Large-Scale Climate Model Dataset for Machine Learning
MALI-Dev / E3SM
Forked from E3SM-Project/E3SMRepository for development of MPAS-Albany-Land Ice (MALI) model. MALI code is located in the `components/mpas-albany-landice` subdirectory of the Energy Exascale Earth System Model (E3SM) repository.
Multitaper codes translated into Python.
Scripts for generating ocean forcing (primarily Antarctic) for the ISMIP6 activity
A Curated List of Python Resources for Earth Sciences
Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you estimate, communicate and use uncertainty in machine learning model predictions.