72 citations · 363 across the 68 of their papers we have counts for
5 papers · 1 filter
Model reduction for a power grid model
Jing Li, Panos Stinis
We apply model reduction techniques to the DeMarco power grid model. The DeMarco model, when augmented by an appropriate line failure mechanism, can be used to study cascade failur…
A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure
Jacob Roth, David A. Barajas-Solano, Panos Stinis +2
In this work, cascading transmission line failures are studied through a dynamical model of the power system operating under fixed conditions. The power grid is modeled as a stocha…
Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning
Panos Stinis
We assume that we are given a time series of data from a dynamical system and our task is to learn the flow map of the dynamical system. We present a collection of results on how t…
A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations
Ramakrishna Tipireddy, Paris Perdikaris, Panos Stinis +1
We investigate the use of discrete and continuous versions of physics-informed neural network methods for learning unknown dynamics or constitutive relations of a dynamical system.…
Improving solution accuracy and convergence for stochastic physics parameterizations with colored noise
Panos Stinis, Huan Lei, Jing Li +1
Stochastic parameterizations are used in numerical weather prediction and climate modeling to help capture the uncertainty in the simulations and improve their statistical properti…