no code implementations • 1 Oct 2023 • Parikshit Pareek, Deepjyoti Deka, Sidhant Misra
Additionally, MT-VDK-GP outperforms a hyper-parameter based transfer learning approach in over 75% of N-2 contingency network structures, even without historical N-2 outage data.
no code implementations • 15 Aug 2023 • Parikshit Pareek, Deepjyoti Deka, Sidhant Misra
This paper presents a physics-inspired graph-structured kernel designed for power flow learning using Gaussian Process (GP).
no code implementations • 17 Oct 2022 • Paprapee Buason, Sidhant Misra, Daniel K. Molzahn
In this paper, we consider a sensor placement problem which seeks to identify locations for installing sensors that can capture all possible violations of voltage magnitude limits.
no code implementations • 4 Dec 2021 • Sarthak Gupta, Sidhant Misra, Deepjyoti Deka, Vassilis Kekatos
Stochastic optimal power flow (SOPF) formulations provide a mechanism to handle these uncertainties by computing dispatch decisions and control policies that maintain feasibility under uncertainty.
1 code implementation • 2 Apr 2021 • Arkopal Dutt, Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra
We observe that for samples coming from a dynamical process far from equilibrium, the sample complexity reduces exponentially compared to a dynamical process that mixes quickly.
1 code implementation • 18 Feb 2021 • Christopher X. Ren, Sidhant Misra, Marc Vuffray, Andrey Y. Lokhov
We address the problem of learning of continuous exponential family distributions with unbounded support.
1 code implementation • NeurIPS 2020 • Abhijith J., Andrey Y. Lokhov, Sidhant Misra, Marc Vuffray
In addition, we also show a variant of NeurISE that can be used to learn a neural net representation for the full energy function of the true model.
no code implementations • 4 May 2020 • Deepjyoti Deka, Harish Doddi, Sidhant Misra, Murti Salapaka
This paper discusses statistical structure estimation in power grids in the "under-excited" regime, where a subset of internal nodes do not have external injection.
1 code implementation • NeurIPS 2020 • Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov
We identify a single condition related to model parametrization that leads to rigorous guarantees on the recovery of model structure and parameters in any error norm, and is readily verifiable for a large class of models.
1 code implementation • 23 Jan 2018 • Yeesian Ng, Sidhant Misra, Line A. Roald, Scott Backhaus
Although the number of possible bases is exponential in the size of the system, we show that only a few of them are relevant to system operation.
Optimization and Control
no code implementations • 15 Mar 2017 • Sidhant Misra, Marc Vuffray, Andrey Y. Lokhov
What is the optimal number of independent observations from which a sparse Gaussian Graphical Model can be correctly recovered?
1 code implementation • 15 Mar 2017 • Kaarthik Sundar, Harsha Nagarajan, Line Roald, Sidhant Misra, Russell Bent, Daniel Bienstock
As renewable wind energy penetration rates continue to increase, one of the major challenges facing grid operators is the question of how to control transmission grids in a reliable and a cost-efficient manner.
Systems and Control
1 code implementation • 15 Dec 2016 • Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra, Michael Chertkov
Reconstruction of structure and parameters of an Ising model from binary samples is a problem of practical importance in a variety of disciplines, ranging from statistical physics and computational biology to image processing and machine learning.
no code implementations • 21 Jun 2016 • Krishnamurthy Dvijotham, Pascal Van Hentenryck, Michael Chertkov, Sidhant Misra, Marc Vuffray
In this paper, we formulate the optimal power flow problem over tree networks as an inference problem over a tree-structured graphical model where the nodal variables are low-dimensional vectors.
no code implementations • NeurIPS 2016 • Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov, Michael Chertkov
We prove that with appropriate regularization, the estimator recovers the underlying graph using a number of samples that is logarithmic in the system size p and exponential in the maximum coupling-intensity and maximum node-degree.
no code implementations • 5 Feb 2016 • David Gamarnik, Sidhant Misra
We consider the problem of reconstructing a low rank matrix from a subset of its entries and analyze two variants of the so-called Alternating Minimization algorithm, which has been proposed in the past.