2 papers
cs.LG2021
Can we learn gradients by Hamiltonian Neural Networks?
Aleksandr Timofeev, Andrei Afonin, Yehao Liu
In this work, we propose a meta-learner based on ODE neural networks that learns gradients. This approach makes the optimizer is more flexible inducing an automatic inductive bias…
stat.ML2021
Which Neural Network to Choose for Post-Fault Localization, Dynamic State Estimation and Optimal Measurement Placement in Power Systems?
Andrei Afonin, Michael Chertkov
We consider a power transmission system monitored with Phasor Measurement Units (PMUs) placed at significant, but not all, nodes of the system. Assuming that a sufficient number of…