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eess.SY2025
Towards Reliable Neural Optimizers: Permutation-Equivariant Neural Approximation in Dynamic Data Driven Applications Systems
Meiyi Li, Javad Mohammadi
Dynamic Data Driven Applications Systems (DDDAS) motivate the development of optimization approaches capable of adapting to streaming, heterogeneous, and asynchronous data from sen…
cs.LG2025
Impact of Data Poisoning Attacks on Feasibility and Optimality of Neural Power System Optimizers
Nora Agah, Meiyi Li, Javad Mohammadi
The increased integration of clean yet stochastic energy resources and the growing number of extreme weather events are narrowing the decision-making window of power grid operators…
eess.SY2025★ 1 cited
Learning to Optimize Joint Chance-constrained Power Dispatch Problems
Meiyi Li, Javad Mohammadi
The ever-increasing integration of stochastic renewable energy sources into power systems operation is making the supply-demand balance more challenging. While joint chance-constra…