Michael Chertkov

research

∙ 09/13/2023

Geospatial Tessellation in the Agent-In-Cell Model: A Framework for Agent-Based Modeling of Pandemic

Agent-based simulation is a versatile and potent computational modeling ...

0 Amir Mohammad Esmaieeli Sikaroudi, et al. ∙

research

∙ 08/14/2023

U-Turn Diffusion

We present a comprehensive examination of score-based diffusion models o...

0 Hamidreza Behjoo, et al. ∙

research

∙ 04/04/2023

Control of Line Pack in Natural Gas System: Balancing Limited Resources under Uncertainty

We build and experiment with a realistic but reduced natural gas model o...

0 Criston Hyett, et al. ∙

research

∙ 03/31/2023

A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study

This paper addresses the challenge of efficiently solving the optimal po...

0 Robert Ferrando, et al. ∙

research

∙ 03/16/2023

Universality and Control of Fat Tails

Motivated by applications in hydrodynamics and networks of thermostatica...

0 Michael Chertkov, et al. ∙

research

∙ 01/25/2023

Exact Fractional Inference via Re-Parametrization Interpolation between Tree-Re-Weighted- and Belief Propagation- Algorithms

Inference efforts – required to compute partition function, Z, of an Isi...

0 Hamidreza Behjoo, et al. ∙

research

∙ 08/27/2022

Statistical Mechanics of Thermostatically Controlled Multi-Zone Buildings

We study the collective phenomena and constraints associated with the ag...

0 Lucas Fuentes Valenzuela, et al. ∙

research

∙ 05/23/2022

Machine Learning for Electricity Market Clearing

This paper seeks to design a machine learning twin of the optimal power ...

0 Laurent Pagnier, et al. ∙

research

∙ 10/26/2021

Model Reduction of Swing Equations with Physics Informed PDE

This manuscript is the first step towards building a robust and efficien...

0 Laurent Pagnier, et al. ∙

research

∙ 10/25/2021

Physics Informed Machine Learning of SPH: Machine Learning Lagrangian Turbulence

Smoothed particle hydrodynamics (SPH) is a mesh-free Lagrangian method f...

0 Michael Woodward, et al. ∙

research

∙ 04/07/2021

Which Neural Network to Choose for Post-Fault Localization, Dynamic State Estimation and Optimal Measurement Placement in Power Systems?

We consider a power transmission system monitored with Phasor Measuremen...

0 Andrei Afonin, et al. ∙

research

∙ 03/26/2021

Embedding Power Flow into Machine Learning for Parameter and State Estimation

Modern state and parameter estimations in power systems consist of two s...

0 Laurent Pagnier, et al. ∙

research

∙ 02/16/2021

Message Passing Descent for Efficient Machine Learning

We propose a new iterative optimization method for the Data-Fitting (DF)...

0 Francesco Concetti, et al. ∙

research

∙ 02/12/2021

Physics-Informed Graphical Neural Network for Parameter State Estimations in Power Systems

Parameter Estimation (PE) and State Estimation (SE) are the most wide-sp...

0 Laurent Pagnier, et al. ∙

research

∙ 01/28/2021

Neural Particle Image Velocimetry

In the past decades, great progress has been made in the field of optica...

0 Nikolay Stulov, et al. ∙

research

∙ 05/05/2020

A Hierarchical Approach to Multi-Energy Demand Response: From Electricity to Multi-Energy Applications

Due to proliferation of energy efficiency measures and availability of t...

0 Ali Hassan, et al. ∙

research

∙ 04/20/2020

Data-Driven Learning and Load Ensemble Control

Demand response (DR) programs aim to engage distributed small-scale flex...

0 Ali Hassan, et al. ∙

research

∙ 10/22/2019

Tractable Minor-free Generalization of Planar Zero-field Ising Models

We present a new family of zero-field Ising models over N binary variabl...

0 Valerii Likhosherstov, et al. ∙

research

∙ 06/14/2019

A New Family of Tractable Ising Models

We present a new family of zero-field Ising models over N binary variabl...

0 Valerii Likhosherstov, et al. ∙

research

∙ 01/03/2019

Learning a Generator Model from Terminal Bus Data

In this work we investigate approaches to reconstruct generator models f...

0 Nikolay Stulov, et al. ∙

research

∙ 12/22/2018

Inference and Sampling of K_33-free Ising Models

We call an Ising model tractable when it is possible to compute its part...

0 Valerii Likhosherstov, et al. ∙

research

∙ 11/12/2018

Gauges, Loops, and Polynomials for Partition Functions of Graphical Models

We suggest a new methodology for analysis and approximate computations o...

0 Michael Chertkov, et al. ∙

research

∙ 10/16/2018

From Deep to Physics-Informed Learning of Turbulence: Diagnostics

We describe physical tests validating progress made toward acceleration ...

0 Ryan King, et al. ∙

research

∙ 10/11/2018

Real-time Fault Localization in Power Grids With Convolutional Neural Networks

Diverse fault types, fast re-closures and complicated transient states a...

0 Wenting Li, et al. ∙

research

∙ 03/17/2018

Topology Estimation using Graphical Models in Multi-Phase Power Distribution Grids

Distribution grid is the medium and low voltage part of a large power sy...

0 Deepjyoti Deka, et al. ∙

research

∙ 03/14/2018

Bucket Renormalization for Approximate Inference

Probabilistic graphical models are a key tool in machine learning applic...

0 Sungsoo Ahn, et al. ∙

research

∙ 01/05/2018

Gauged Mini-Bucket Elimination for Approximate Inference

Computing the partition function Z of a discrete graphical model is a fu...

0 Sungsoo Ahn, et al. ∙

research

∙ 10/27/2017

Online Learning of Power Transmission Dynamics

We consider the problem of reconstructing the dynamic state matrix of tr...

0 Andrey Y. Lokhov, et al. ∙

research

∙ 10/20/2017

Belief Propagation Min-Sum Algorithm for Generalized Min-Cost Network Flow

Belief Propagation algorithms are instruments used broadly to solve grap...

0 Andrii Riazanov, et al. ∙

research

∙ 10/19/2017

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...

0 Art B. Owen, et al. ∙

research

∙ 07/07/2017

Methodology for Multi-stage, Operations- and Uncertainty-Aware Placement and Sizing of FACTS Devices in a Large Power Transmission System

We develop new optimization methodology for planning installation of Fle...

0 Vladimir Frolov, et al. ∙

research

∙ 07/05/2017

Topology Estimation in Bulk Power Grids: Guarantees on Exact Recovery

The topology of a power grid affects its dynamic operation and settlemen...

0 Deepjyoti Deka, et al. ∙

research

∙ 03/03/2017

Gauging Variational Inference

Computing partition function is the most important statistical inference...

0 Sungsoo Ahn, et al. ∙

research

∙ 06/21/2016

Graphical Models for Optimal Power Flow

Optimal power flow (OPF) is the central optimization problem in electric...

0 Krishnamurthy Dvijotham, et al. ∙

research

∙ 05/29/2016

MCMC assisted by Belief Propagaion

Markov Chain Monte Carlo (MCMC) and Belief Propagation (BP) are the most...

0 Sungsoo Ahn, et al. ∙

research

∙ 05/24/2016

Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models

We consider the problem of learning the underlying graph of an unknown I...

0 Marc Vuffray, et al. ∙

research

∙ 09/23/2015

Minimum Weight Perfect Matching via Blossom Belief Propagation

Max-product Belief Propagation (BP) is a popular message-passing algorit...

0 Sungsoo Ahn, et al. ∙

research

∙ 02/03/2015

Learning Planar Ising Models

Inference and learning of graphical models are both well-studied problem...

0 Jason K. Johnson, et al. ∙

research

∙ 08/09/2014

Approximate inference on planar graphs using Loop Calculus and Belief Propagation

We introduce novel results for approximate inference on planar graphical...

0 vicen.gomez@upf.edu, et al. ∙

research

∙ 06/05/2013

Loop Calculus and Bootstrap-Belief Propagation for Perfect Matchings on Arbitrary Graphs

This manuscript discusses computation of the Partition Function (PF) and...

0 Michael Chertkov, et al. ∙

research

∙ 05/17/2013

Belief Propagation for Linear Programming

Belief Propagation (BP) is a popular, distributed heuristic for performi...

0 Andrew Gelfand, et al. ∙

Michael Chertkov

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