Develops a position-conditioned offline RL architecture with Point Attention for extracting policies that generalize across varying sensor placements in fluid control tasks.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
ML surrogates (KNN and neural nets with custom loss) generate interconnector flows that enable reduced PSOMs to match full European simulation outcomes at up to 500x speedup.
citing papers explorer
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Offline Reinforcement Learning for Fluid Controls: Data-based Multi-observational Policy Extraction
Develops a position-conditioned offline RL architecture with Point Attention for extracting policies that generalize across varying sensor placements in fluid control tasks.
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Surrogate Modeling of Interconnector Flows: A Machine Learning Alternative to Full-Scale Power System Simulations with Application to Cross-Border Electricity Exchange
ML surrogates (KNN and neural nets with custom loss) generate interconnector flows that enable reduced PSOMs to match full European simulation outcomes at up to 500x speedup.