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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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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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those described by which offer a promising architecture for modelling population-level neural interactions. Prior work has emphasized rate-based codes due to their relative simplicity; our approach will
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planning and execution architecture for information-driven experiment steering (closed-loop control) Work in an interdisciplinary team of engineers, computer scientists, and life scientists Present your work
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to neural population coding. As a starting point, we will build upon recent advances in graph neural networks (GNNs), particularly those described by which offer a promising architecture for modelling
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information about our institute here: https://www.fz-juelich.de/en/ias/ias-8 Your Job: Develop physics-aware simulations of growing cell populations, including their spatiotemporal manipulation in microfluidic