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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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efficient model parallel training Implementation and optimization of GPU-accelerated training pipelines Validation of models on patient-specific geometries obtained from MRI data Participation in conferences
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prediction Integration of domain decomposition methods into the learning framework to enable efficient model parallel training Implementation and optimization of GPU-accelerated training pipelines Validation
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to accelerate solving AC power flow (AC-PF) computations, potentially facilitating real‑time contingency analysis, rapid design‑space exploration, and on‑line operational optimization of power systems
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of the original computations at a fraction of the cost. This hybridization aims not only to accelerate performance but also to maintain, if not improve, analytical rigor. The improved modules will be integrated
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to accelerate performance but also to maintain, if not improve, analytical rigor. The improved modules will be integrated into an updated analytical pipeline and validated against benchmark datasets drawn from