Projects

The doctoral network Artificial Intelligence in Parkinson’s Disease (AIPD) is researching new AI-based methods for the early diagnosis and treatment of Parkinson’s disease. The goal is to develop digital approaches that enable more precise detection of Parkinson's disease – for example, by identifying speech biomarkers – and thereby support more personalized therapies. The project also addresses issues of trustworthiness, ethics, and the regulation of AI solutions.

The integration of recycled materials continues to pose challenges for the manufacturing industry, as the quality of the products depends on the interaction between the materials used and the manufacturing processes. Variations in trace elements and chemical properties affect additive manufacturing processes such as 3D printing. The GEAR-UP project aims to develop digital tools to facilitate the use of recycled materials in metal and plastics processing. Simulation-based approaches and AI methods will be used to establish resource-efficient manufacturing processes. The digital product passport developed in the project ensures the traceability of materials.

The SYNTHIA project team develops new techniques for the responsible generation and use of synthetic patient data. These data, generated using generative methods of artificial intelligence, can help overcome data protection hurdles, improve prediction models for personalized medicine, and emulate control groups in clinical studies. The validated synthetic data will then be made available on a modern IT platform, together with their possible applications.

Today, most crash tests to evaluate vehicle safety are conducted virtually. Documenting the changes to the simulated vehicle models is particularly time-consuming and costly. The SAFECAR-ML project aims to simplify this process. By combining novel methods of artificial intelligence (AI) with technical knowledge from vehicle development, the project partners from research and automotive industry want to standardize the information processing for the documentation of virtual crash tests. New is the combination of semantically processed free text with multimodal engineering data for machine learning.

Technologies such as CCUS (Carbon Capture, Utilisation, and Storage), which capture and store CO₂, are a key component of decarbonization strategies. The project "Eastern Lights" supports the development of a large-scale CCUS cluster in northern Bulgaria to store CO₂ from the cement industry. The construction of a short pipeline is planned to safely transport CO₂ to saline aquifers for storage. The goal is to establish an efficient transport and storage infrastructure that will serve as a long-term model for CO₂ utilization in the region. Fraunhofer SCAI’s software MYNTS supports the planning and optimization of the CO₂ pipeline network.

The Marie Skłodowska-Curie Doctoral Network PREDICTOR is developing new AI-powered methods for the fast and targeted discovery of electroactive materials for redox flow batteries. The objective is to rapidly identify promising electrolytes by integrating simulation, automated synthesis, high-throughput experimental testing, and intelligent data management. Fraunhofer SCAI contributes semantic concepts, ontologies, and AI-based prediction models that connect and exploit the wealth of project data to enable targeted material design.

The BASE project aims to develop the digital battery passport, which every larger battery will be required to have in the future. The passport will contain continuously collected data on the "State of Health" as well as information on the supply chain, the manufacturing process and material data. It will apply the "mass balance" method, which offers a detailed accounting of the materials used in battery production, with special emphasis on the use of sustainable components. The data from the battery passport is stored in a decentralized and tamper-proof manner so that all parties involved have access to it. This enables the optimization of battery lifespan and enhances recycling.

The main objective of the ETHCSTWIN initiative is to establish a collaborative network between the Institute of Ethnopharmacological Studies and Phytotherapy (IESP, Athens) and two renowned academic research teams from Italy (UNISG, Pollenzo) and Germany (Fraunhofer SCAI), as well as the biotech SME Pangea Botanica and the University of Prishtina. The goal is to integrate the knowledge of ethnopharmacology into novel computational and digital systems, focusing on the development of a rich portfolio of complex methods and tools, including the analysis of large data sets and the restoration of tangible and intangible heritage.

The SmartEM project develops a standardized system that allows to combine different computational engineering models from different sources and to merge them into a complete system. This flexible reference architecture, inspired by open data space concepts such as Gaia-X, promotes collaboration between different actors and enables the reuse of engineering models. This makes development processes more efficient and replaces manual, time-consuming procedures for creating digital twins. The AI-based generation of "surrogate models" - simplified versions of complex models from heterogeneous data sources - facilitates model integration and thus improves interoperability. In this way, SmartEM accelerates digital transformation and innovation in engineering.

The DeployAI project aims to build and operate a European AI on Demand Platform (AIoDP). To this end, DeployAI brings together industry representatives and research institutions. The aim is to provide trustworthy, ethical, and transparent European AI solutions for use in industry – especially small and medium enterprises – and the public sector.

The ALABAMA project is developing adaptive laser techniques for improved additive manufacturing. The goal is to decrease material porosity and adjust microstructures. The innovation includes the development of models for process optimization. Process parameters are optimized using multi-beam control and laser shaping. Advanced monitoring uses multispectral imaging for quality control. Material tests ensure compliance with requirements. The technology is being tested in aerospace, maritime, and automotive industries. These sectors face challenges regarding material quality. The project promises significant productivity increases and cost reductions. It also promotes the autonomy of the European industry.

Remanufacturing boosts circular economies and job creation. RESTORE enhances this with sustainable processes and materials. It merges advanced technologies for better remanufacturing applications. The RESTORE platform will digitize and streamline the remanufacturing process. Fraunhofer SCAI standardizes data for remanufacturing efficiency.

This project aims to create a multi-scale patient-specific human virtual twin for patients eligible for CAR T cell therapy, which can be extended to cellular immunotherapies in general.

Fraunhofer SCAI coordinates the COMMUTE project, backed by a grant from the European Commission. Over the next four years, an interdisciplinary team of top-tier experts will explore whether COVID-19 infections increase the risk of acquiring neurodegenerative diseases. An innovative AI-driven system is being developed to provide tailored risk assessments for individuals who have recovered from COVID-19.

Over the next four years, PREDICTOM will develop a platform for assessing dementia risks. The platform will enable early detection, allowing timely intervention and preventive treatment.

The overall aim of COMMITMENT is to develop a computational systems medicine framework that allows clinically meaningful stratification of psychotic disorders and the identification of biological processes shared with somatic and neurodegenerative comorbidities.

Recent years have seen an explosion in health data generation from diverse sources. This data has the potential to help advance both research and patient care. However, accessing, integrating, and analyzing it is exceptionally challenging. IDERHA aims to set up an open platform that will facilitate the integration and analysis of diverse types of health data. The platform will link up multiple public and private data sources and implement interoperable tools and services to enable key groups, such as doctors, patients, and researchers, to use the data. To focus their efforts, the IDERHA team will use lung cancer as a use case to design the platform.

A further increase in the performance of supercomputers is expected over the next few years. So-called exascale computers will then be able to deliver more accurate simulation results. Fraunhofer SCAI is developing efficient data analysis methods for the much larger amounts of data generated in this way, which will also provide the engineer with detailed insights into the complex technical interrelationships.

The Real4Reg project, coordinated by the German Federal Institute for Drugs and Medical Devices (BfArM), aims to analyze billing data with the help of AI to generate better insights into drug side effects and interactions.

PIONEER aims to develop and implement an interoperable material modeling and manufacturing ecosystem that enables multi-directional data flow along the material value chain by linking product design and distributed modeling data with information from material characterization, manufacturing processes, and product quality criteria. PIONEER combines a design-by-simulation approach with manufacturing and quality data to optimize product development strategies in high-mix/low-volume production systems.

The service center "WestAI" is a consortium of scientific organizations in North Rhine-Westphalia led by the University of Bonn. The services offered to customers from science and industry include support in the processing of data, in the transfer of AI technologies, in preliminary research and in the development of prototypes and customer-specific AI solutions.

The PADME-AM, »Partition der Eins Methoden für Additive Manufacturing« (Partition of One Methods for Additive Manufacturing), project is developing new simulation methods for 3D printing processes in the field of laser-based powder bed fusion (L-PBF). L-PBF processes offer the possibility of printing complex components with high quality and low weight for the medical and aerospace industries.

Patients with severe psychiatric disorders such as schizophrenia, bipolar disorder and clinical depression often develop resistance to drug therapy. Even when the early signs of treatment resistance (TR) are detected, patients must undergo lengthy procedures before medical professionals can prescribe adequate pharmaceutical care. The EU-funded Psych-STRATA project intends to analyse extensive clinical, genetic and biological data of psychiatric patients to establish criteria for early detection of TR. Based on the findings, it will propose treatment strategies for patients at risk of TR. The project will further create machine learning models that can predict TR risk and patient response to treatment, thus assisting medical professionals in providing more personalised treatment.

ParKInsonPredict is a joint research project of Technische Universität Dresden and Fraunhofer SCAI aiming to predict disease progression patterns in Parkinson's disease.

REMEDi4ALL aims to make a significant leap forward in drug repurposing. This promising approach to drug development, consisting of identifying, testing, and validating new therapeutic indications for existing medications, is a developing field but faces numerous barriers and systemic inefficiencies. Still, its potential to significantly bring down times and costs of drug development - it focuses on already approved, discontinued, shelved or investigational therapeutics - makes this novel strategy attractive for rare and neglected conditions, cancer, emerging public health threats such as COVID-19 or new drug combinations. It also translates into more sustainable health systems.

The project aims to develop a decentralized, data protection-compliant research platform capable of simulating some of the brain’s complex neurobiological phenomena. As part of the project, researchers will collate various types of information, including data from PET and MRI scans, EEG tests, behavioral studies, lifestyle surveys, and clinical data from thousands of patients and healthy controls. These will be combined with biological information from knowledge databases and made available for research. The resultant digital ‘brain twins’ will enable many researchers to conduct innovative research within a powerful digital infrastructure.

ADIS stands for "Early Diagnosis of Alzheimer's Disease by Immune Profiling of Cytotoxic Lymphocytes and Recording of Sleep Disturbances." The project is funded by the EU Joint Programme for Neurodegenerative Diseases Research (JPND). JPND is the largest global research initiative to address neurodegenerative disease challenges. The ADIS project (start: July 2022) will run for three years and has a budget of 1.3 million euros, of which 300,000 euros will go to Fraunhofer SCAI.

We aim to validate and commercialise biomarkers and the resulting products for affective disorders to address the many unmet needs along the entire disease trajectory.

The goal of the AIOLOS project (Artificial Intelligence Tools for Outbreak Detection and Response) is to develop a digital platform to allow for early detection of new respiratory pathogens epidemics, monitor their spread, and inform decision-makers on appropriate countermeasures. AIOLOS will provide insights for private and public decision-making in a web-based dashboard, which leverages real-time data from multiple data sources, advanced artificial intelligence (AI), and predictive modeling.

This joint laboratory project between Fraunhofer SCAI, the University of Applied Sciences Bonn-Rhein-Sieg, and the Dr. Reinold Hagen Foundation falls precisely at the center of this topic. It aims to create new digital-twin-related insights and turn them into benefits for small and medium-sized companies that experience exceptionally high digitalization pressure. These companies are often regional, e.g., experts in niche manufacturing techniques. It is particularly challenging for such companies to gather sufficient expertise in as many disciplines as required to these ends.