All software
SeisBench: A toolbox for machine learning in seismology
SeisBench is an open-source Python toolbox for machine learning in seismology. It brings together the whole machine learning model lifecycle: datasets and benchmarks, models and training pipelines, and efficient implementations for deploying the models in production.
- Earth & Environment
- GPU
- Helmholtz AI
- + 4
- Jupyter Notebook
- Python
- C
openCARP
openCARP is a multiscale cardiac electrophysiology simulator for in silico experiments ranging from single heart cells and cardiac tissue to organ models up to the body surface ECG.
- computational cardiology
- digital twin
- electrophysiology
- + 3
- C++
- Python
- CMake
- + 2
4C Multiphysics
4C is a parallel multiphysics research code for simulating solid/fluid mechanics, scalar transport, and chemical reactions. It supports both single-field and coupled systems, offering ready-to-use solutions for a wide range of physical problems.
- Computational Fluid Dynamics
- High performance computing
- Information
- + 3
- C++
- CMake
- Python
- + 6
AROSICS
AROSICS is an automated and robust open-source image co-registration software for multi-sensor satellite data.
- Earth & Environment
- Fourier Shift Theorem
- Geometric Pre-processing
- + 8
- Python
- Makefile
- Shell
KaHyPar
KaHyPar is a fast, high-quality, and scalable algorithm for partitioning graphs and hypergraphs with billions of edges. It finds applications in minimizing communication costs for distributed (hyper)graph computations, quantum circuit simulations, storage sharding in databases, and VLSI design.
- Clustering
- Data analysis
- Graphs
- + 8
AMIRIS
AMIRIS is the open Agent-based Market model for the Investigation of Renewable and Integrated energy Systems. It aims at enabling scientists to dissect the complex questions arising with respect to future energy markets, their market design, and energy-related policy instruments.
- Agent-based Model
- electricity
- Energy
- + 4
- Java
- Python
- BibTeX
MIRP
MIRP is a python package for quantitative analysis of medical images. It focuses on processing images for integration with radiomics workflows. These workflows either use quantitative features computed using MIRP, or directly use MIRP to process images as input for deep learning models.
- Medical Image Processing
- python
- radiomics
- Python
- Jupyter Notebook
- R
- + 2
EnrichedHeatmap
Enriched heatmap is a special type of heatmap which visualizes the enrichment of genomic signals on specific target regions. The EnrichedHeatmap package provides advanced solutions for normalizing genomic signals within target regions as well as offering highly customizable visualizations.
- Data analysis
- Data Visualization
- FAIR Data
- + 2
- R
- CSS
- C++
FastSurfer
FastSurfer is a fast and accurate deep-learning pipeline for the analysis of human brain MRI. FastSurfer provides a fully compatible FreeSurfer alternative for volumetric and surface-based thickness analysis, also supporting sub-mm resolutions, and sub-segmentation of neuroanatomical structures.
- Data analysis
- Deep Learning
- Image processing
- + 3
- Python
- Jupyter Notebook
- Shell
- + 1
CryoGrid
The CryoGrid Model Suite provides a set of numerical tools for simulating the thermal soil regime and the ice-water balance for permafrost and glaciers. Depending on the application needs different model tools are offered, characterized by different levels complexity and modularity.
- Climate
- Earth & Environment
- Modeling
- + 1
- MATLAB
- HTML
- Mercury
- + 2
Materials Learning Algorithms
MALA (Materials Learning Algorithms) is a data-driven framework to accelerate electronic structure calculations based on machine learning. Its purpose is to construct neural-network surrogate models for bypassing computationally expensive steps in state-of-the-art density functional simulations.
- Density functional theory
- electronic structure
- machine-learning
- + 2
- Python
- Fortran
- Shell
- + 1
CoMOLA
CoMOLA is a python tool for optimal spatial allocation of land uses. The tool creates optimal land-use maps for up to four objectives. It takes into account constraints and can be coupled with external models to evaluate different objectives (e.g. ecosystem services/biodiversity models)
- land-use allocation
- multi-objective optimization
- NSGA-II
- Python
- R