2 citations · 3 across the 3 of their papers we have counts for
4 papers
Learning to Optimize meets Neural-ODE: Real-Time, Stability-Constrained AC OPF
Vincenzo Di Vito, Mostafa Mohammadian, Kyri Baker +1
Recent developments in applying machine learning to address Alternating Current Optimal Power Flow (AC OPF) problems have demonstrated significant potential in providing close to o…
Learning To Solve Differential Equation Constrained Optimization Problems
Vincenzo Di Vito, Mostafa Mohammadian, Kyri Baker +1
Differential equations (DE) constrained optimization plays a critical role in numerous scientific and engineering fields, including energy systems, aerospace engineering, ecology,…
Learning Joint Models of Prediction and Optimization
James Kotary, Vincenzo Di Vito, Jacob Cristopher +2
The Predict-Then-Optimize framework uses machine learning models to predict unknown parameters of an optimization problem from exogenous features before solving. This setting is co…
Predict-Then-Optimize by Proxy: Learning Joint Models of Prediction and Optimization
James Kotary, Vincenzo Di Vito, Jacob Christopher +2
Many real-world decision processes are modeled by optimization problems whose defining parameters are unknown and must be inferred from observable data. The Predict-Then-Optimize f…