AI-enhanced differentiable Ray Tracer for Irradiation-prediction in Solar Tower Digital Twins - ARTIST
Solar tower power plants play a key role in facilitating the ongoing energy transition as they deliver dispatchable climate neutral electricity and direct heat for chemical processes. In this work we develop a heliostat-specific differentiable ray tracer capable of modeling the energy transport at the solar tower in a data-driven manner. This enables heliostat surface reconstruction and thus drastically improved the irradiance prediction. Additionally, such a ray tracer also drastically reduces the required data amount for the alignment calibration. In principle, this makes learning for a fully AI-operated solar tower feasible. The desired goal is to develop a holistic AI-enhanced digital twin of the solar power plant for design, control, prediction, and diagnosis, based on the physical differentiable ray tracer. Any operational parameter in the solar field influencing the energy transport may be, optimized with it. For the first time gradient-based, e.g., field design, aim point control, and current state diagnosis are possible. By extending it with AI-based optimization techniques and reinforcement learning algorithms, it should be possible to map real, dynamic environmental conditions with low-latency to the twin. Finally, due to the full differentiability, visual explanations for the operational action predictions are possible. The proposed AI-enhanced digital twin environment will be verified at a real power plant in Jülich. Its inception marks a significant step towards a fully automatic solar tower power plant.
Holistic Imaging and Molecular Analysis in life-threatening Ailments - HIMALAYA
since 2024-02-01 - 2027-01-31
The overall goal of this project is to improve the radiological diagnosis of human prostate cancer in clinical MRI by AI-based exploitation of information from higher resolution modalities. We will use the brilliance of HiP-CT imaging at beamline 18 and an extended histopathology of the entire prostate to optimise the interpretation of MRI images in the context of a research prototype. In parallel, the correlation of the image data with the molecular properties of the tumours is planned for a better understanding of invasive tumour structures. An interactive multi-scale visualisation across all modalities forms the basis for vividly conveying the immense amounts of data. As a result, at the end of the three-year project phase, the conventional radiological application of magnetic resonance imaging (MRI) is to be transferred into a diagnostic standard that also reliably recognises patients with invasive prostate tumours that have often been incorrectly diagnosed to date, taking into account innovative AI algorithms. In the medium term, a substantial improvement in the care of patients with advanced prostate carcinoma can therefore be expected. In addition, we will make the unique multimodal data set created in the project, including visualisation tools, available as open data to enable further studies to better understand prostate cancer, which could potentially lead to novel diagnostic and therapeutic approaches.
Identity & Access Management for the National Research Data Infrastructure - IAM4NFDI
Identity and Access Management (IAM) is concerned with the processes, policies and technologies for managing digital identities and their access rights to specific resources. Providing IAM as a basic serivce, IAM4NFDI is funded by Base4NFDI.
The task of the Basic Service IAM is to establish and provide a state-of-the-art AAI, that fosters cross-consortial and international collaboration.
A central goal of IAM4NFDI is therefore enable unified access to data, software, and compute resources, as well as sovereign data exchange and collaborative work for all NFDI consortia, as well as compatible initiatives. In order to achieve this, an extended version of the AARC Blueprint Architecture will be implemented an provided as a service to interested NFDI consortia.
This will enable researchers from different domains and institutions to access digital resources within and beyond NFDI. Users from approx. 400 German research and higher education institutions plus approx. 4800 home organisations worldwide will be able to access services and resources provided by the NFDI Community AAI. Also google, github, or ORCID accounts are enabled to access specific parts of the provided services.
All tools and solutions developed within IAM4NFDI are compatible with standards and the AARC Recommendations.
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