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methods for their bottlenecks, these steps will then be replaced or supplemented with ML-based surrogates or approximators, such as random forests or shallow neural networks, trained to mimic the outputs
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particular, we aim to develop a neural network architecture that will allow us to accelerate solving AC power flow (AC-PF) computations, potentially facilitating real‑time contingency analysis, rapid design
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network approaches for stress prediction in arterial walls and plaque. Another part of the project is exploring the use of large language models to support neural network design and data preprocessing
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surrogates or approximators, such as random forests or shallow neural networks, trained to mimic the outputs of the original computations at a fraction of the cost. This hybridization aims not only
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on the contributions of specific cortical layers and neural activity in various brain regions. Are task-specific submanifolds or spike correlation patterns limited to local neural circuits or span across brain regions
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, extending them with physics-based approaches, and adapting existing physics-integrated neural network approaches for stress prediction in arterial walls and plaque. Another part of the project is exploring
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Research Institute - Kobe, Hyogo Advanced ICT Research Institute - Suita, Osaka Center for Information and Neural Networks - Nihonbashi, Tokyo Innovation Center - Sendai, Miyagi Resilient ICT Research Center
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, Osaka Center for Information and Neural Networks - Nihonbashi, Tokyo Innovation Center - Sendai, Miyagi Resilient ICT Research Center - Kashima, Ibaraki Kashima Space Technology Center - Nomi, Ishikawa
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instances to solve new, yet similar, instances more efficiently than with general purpose algorithms such as Netwon`s method. In particular, we aim to develop a neural network architecture that will allow us
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education and networking opportunities specifically for doctoral researchers via JuDocS, the Jülich Center for Doctoral Researchers and Supervisors: www.fz-juelich.de/judocs Targeted services