Training AI algorithms requires a great amount of data. However, raw data often contains sensitive information. Homomorphic encryption offers a solution for secure machine learning - with protected sensitive data.
Renewable energies, such as hydropower, are an essential backbone for a climate-friendly future. To enable better real-time monitoring and thus maintenance of hydropower infrastructure, innovative digital hydropower twins are being developed in this project.
The Rail4Future project is focusing on the design of a digital rail system for the future. To this end, a novel and fully virtual validation platform for large-scale simulations of entire rail lines is being developed to increase the efficiency of existing rail infrastructure.
The aim of the application project IVC Multi is to research novel intelligent visual computing methods supporting decision-making in automotive industry, medicine, and life sciences based on ensembles of heterogeneous, multi-scale and/or multi-temporal data.
The aim of the project IVC Stream is to research novel visual computing solutions for simulation and measurement data.
This project aims at accelerating and automating image-based decision making with an application focus on medicine, recycling and quality assurance processes in manufacturing.
The strategic project of the area is the organizational and scientific hub for the realization of the area-wide intelligent visual computing approach for analytics and modelling based on ensembles of dense grid-based data, derived data, and digital embedding.
The research goal of AMASE is to create a suite of tools and methods to ingest, process, visualize, and manipulate heterogeneous, large-scale geospatial data. This data is the constantly updated representation of the real world in the form of an evolving digital twin.
The Raincloud project's goal is to optimize and extend the simulation software Visdom to tackle the increasing challenges in water planning and disaster management in the face of climate change.
The primary goal of project INGRESS is to accelerate and improve the process of data scientists working with Industry 4.0 and Internet of Things (IoT) data, by enabling a closer integration of visual analysis into the existing workflows.
The RAILING project deals with the research and development of interactive, scalable and trust-building visualization and analysis tools for the exploration of time-dependent and complex data.
The main goal of this project is to enable a reliable decision support for large-scale infrastructure projects by providing solutions for a collaborative visual analysis of digital twins.