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Compact Optimality Verification for Optimization Proxies
Recent years have witnessed increasing interest in optimization proxies, i. e., machine learning models that approximate the input-output mapping of parametric optimization problems and return near-optimal feasible solutions.
Just-In-Time Learning for Operational Risk Assessment in Power Grids
In a grid with a significant share of renewable generation, operators will need additional tools to evaluate the operational risk due to the increased volatility in load and generation.
Risk-Aware Control and Optimization for High-Renewable Power Grids
The transition of the electrical power grid from fossil fuels to renewable sources of energy raises fundamental challenges to the market-clearing algorithms that drive its operations.
Uncertainty Quantification
Vocal Bursts Intensity Prediction
