All software

XRAYPAC

A software package for modeling x-ray-induced dynamics of matter

CPlantBox

CPlantBox is a functional-structural plant-soil modelling framework. It simulated the 3D architecture of a plant, coupled carbon and water flow within the plant, water flow and solute transport in a 3D dynamic soil, as well as water and nutrient uptake, and rhizodeposition.

CADET

CADET provides hierarchical models, efficient solvers and computational engineering tools for a range of biotechnological processes, including chromatography, filtration, crystallization, and fermentation.

Earth System Model Evaluation Tool (ESMValTool)

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.

AROSICS

AROSICS is an automated and robust open-source image co-registration software for multi-sensor satellite data.

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.

JURASSIC

The Juelich Rapid Spectral Simulation Code (JURASSIC) is a fast infrared radiative transfer model for the analysis of atmospheric remote sensing measurements.

ARX - Open Source Data Anonymization Software

ARX is an open-source tool for anonymizing sensitive personal data. It offers many privacy/risk models, transformation methods, and utility analyses. Used in research, clinical trials, training, and big-data platforms. Scales to large datasets and includes an intuitive cross-platform GUI.

GOLEM

GOLEM is a modelling platform for thermal-hydraulic-mechanical and non-reactive chemical processes in fractured and faulted porous media, based on the MOOSE framework

Allpix Squared

Allpix Squared is a semiconductor detector simulation framework. The goal of the framework is to provide a complete and easy-to-use package for end-to-end simulations of semiconductor detector performance from incident ionizing radiation until the digitization of hits in the detector chip.

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.