A physics-informed neural representation is learned from safe data to support distributional hypothesis testing for dynamical instability in stochastic DAE systems without repeated simulations.
Gen-dfl: Decision-focused generative learning for robust decision making
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
A data-driven tri-level adaptive robust optimization model with a scalable column-and-constraint generation algorithm jointly optimizes long-term grid configuration and short-term operational mitigation for wildfire ignition uncertainty, validated on synthetic data and a large utility distribution系统
Introduces a decision-aware neural ODE model that integrates outage dynamics prediction with global optimization of resilience interventions for power grids.
citing papers explorer
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Learning to Test: Physics-Informed Representation for Dynamical Instability Detection
A physics-informed neural representation is learned from safe data to support distributional hypothesis testing for dynamical instability in stochastic DAE systems without repeated simulations.
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Large-Scale Resilience Planning for Wildfire-Prone Electricity-System via Adaptive Robust Optimization
A data-driven tri-level adaptive robust optimization model with a scalable column-and-constraint generation algorithm jointly optimizes long-term grid configuration and short-term operational mitigation for wildfire ignition uncertainty, validated on synthetic data and a large utility distribution系统
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Global-Decision-Focused Neural ODEs for Proactive Grid Resilience Management
Introduces a decision-aware neural ODE model that integrates outage dynamics prediction with global optimization of resilience interventions for power grids.