
A Hierarchical Approach to MultiEnergy Demand Response: From Electricity to MultiEnergy Applications
Due to proliferation of energy efficiency measures and availability of t...
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DataDriven Learning and Load Ensemble Control
Demand response (DR) programs aim to engage distributed smallscale flex...
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Tractable Minorfree Generalization of Planar Zerofield Ising Models
We present a new family of zerofield Ising models over N binary variabl...
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A New Family of Tractable Ising Models
We present a new family of zerofield Ising models over N binary variabl...
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Learning a Generator Model from Terminal Bus Data
In this work we investigate approaches to reconstruct generator models f...
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Inference and Sampling of K_33free Ising Models
We call an Ising model tractable when it is possible to compute its part...
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Gauges, Loops, and Polynomials for Partition Functions of Graphical Models
We suggest a new methodology for analysis and approximate computations o...
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From Deep to PhysicsInformed Learning of Turbulence: Diagnostics
We describe physical tests validating progress made toward acceleration ...
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Realtime Fault Localization in Power Grids With Convolutional Neural Networks
Diverse fault types, fast reclosures and complicated transient states a...
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Topology Estimation using Graphical Models in MultiPhase Power Distribution Grids
Distribution grid is the medium and low voltage part of a large power sy...
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Bucket Renormalization for Approximate Inference
Probabilistic graphical models are a key tool in machine learning applic...
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Gauged MiniBucket Elimination for Approximate Inference
Computing the partition function Z of a discrete graphical model is a fu...
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Online Learning of Power Transmission Dynamics
We consider the problem of reconstructing the dynamic state matrix of tr...
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Belief Propagation MinSum Algorithm for Generalized MinCost Network Flow
Belief Propagation algorithms are instruments used broadly to solve grap...
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Importance sampling the union of rare events with an application to power systems analysis
This paper presents a method for estimating the probability μ of a union...
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Methodology for Multistage, Operations and UncertaintyAware Placement and Sizing of FACTS Devices in a Large Power Transmission System
We develop new optimization methodology for planning installation of Fle...
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Topology Estimation in Bulk Power Grids: Guarantees on Exact Recovery
The topology of a power grid affects its dynamic operation and settlemen...
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Gauging Variational Inference
Computing partition function is the most important statistical inference...
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Graphical Models for Optimal Power Flow
Optimal power flow (OPF) is the central optimization problem in electric...
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MCMC assisted by Belief Propagaion
Markov Chain Monte Carlo (MCMC) and Belief Propagation (BP) are the most...
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Interaction Screening: Efficient and SampleOptimal Learning of Ising Models
We consider the problem of learning the underlying graph of an unknown I...
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Minimum Weight Perfect Matching via Blossom Belief Propagation
Maxproduct Belief Propagation (BP) is a popular messagepassing algorit...
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Learning Planar Ising Models
Inference and learning of graphical models are both wellstudied problem...
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Approximate inference on planar graphs using Loop Calculus and Belief Propagation
We introduce novel results for approximate inference on planar graphical...
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Loop Calculus and BootstrapBelief Propagation for Perfect Matchings on Arbitrary Graphs
This manuscript discusses computation of the Partition Function (PF) and...
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Belief Propagation for Linear Programming
Belief Propagation (BP) is a popular, distributed heuristic for performi...
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Michael Chertkov
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Technical Staff Member at Los Alamos National Laboratory