Search Results for author:
Found 45 papers, 3 papers with code
Space-Time Bridge-Diffusion
In this study, we introduce a novel method for generating new synthetic samples that are independent and identically distributed (i. i. d.)
System-Wide Emergency Policy for Transitioning from Main to Secondary Fuel
Inspired by the challenges of running the Israel's power system -- with its increasing integration of renewables, significant load uncertainty, and primary reliance on natural gas -- we investigate an emergency scenario where there's a need to transition temporarily to a pricier secondary fuel until the emergency resolves.
U-Turn Diffusion
We present a comprehensive examination of score-based diffusion models of AI for generating synthetic images.
A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study
This paper addresses the challenge of efficiently solving the optimal power flow problem in real-time electricity markets.
Universality and Control of Fat Tails
Motivated by applications in hydrodynamics and networks of thermostatically-control loads in buildings we study control of linear dynamical systems driven by additive and also multiplicative noise of a general position.
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 Ising model over a graph of $N$ ``spins" -- are most likely exponential in $N$.
Machine Learning for Electricity Market Clearing
This paper seeks to design a machine learning twin of the optimal power flow (OPF) optimization, which is used in market-clearing procedures by wholesale electricity markets.
Towards Model Reduction for Power System Transients with Physics-Informed PDE
This manuscript reports the first step towards building a robust and efficient model reduction methodology to capture transient dynamics in a transmission level electric power system.
Physics informed machine learning with Smoothed Particle Hydrodynamics: Hierarchy of reduced Lagrangian models of turbulence
Within this hierarchy, two new parameterized smoothing kernels are developed in order to increase the flexibility of the learn-able SPH simulators.
BIG-bench Machine Learning
Physics-informed machine learning
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 Measurement Units (PMUs) placed at significant, but not all, nodes of the system.
Embedding Power Flow into Machine Learning for Parameter and State Estimation
Modern state and parameter estimations in power systems consist of two stages: the outer problem of minimizing the mismatch between network observation and prediction over the network parameters, and the inner problem of predicting the system state for given values of the parameters.
Message Passing Descent for Efficient Machine Learning
We suggest the {\bf Message Passage Descent} algorithm which relies on the piece-wise-polynomial representation of the model DF function.
Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems
Parameter Estimation (PE) and State Estimation (SE) are the most wide-spread tasks in the system engineering.
Neural Particle Image Velocimetry
In this work, we introduce a convolutional neural network adapted to the problem, namely Volumetric Correspondence Network (VCN) which was recently proposed for the end-to-end optical flow estimation in computer vision.
Super-relaxation of space-time-quantized ensemble of energy loads to curtail their synchronization after demand response perturbation
However, this also results in the parasitic synchronization of individual devices within the ensemble leading to long post-demand-response oscillations in the integrated energy consumption of the ensemble.
A Hierarchical Approach to Multi-Energy Demand Response: From Electricity to Multi-Energy Applications
Due to proliferation of energy efficiency measures and availability of the renewable energy resources, traditional energy infrastructure systems (electricity, heat, gas) can no longer be operated in a centralized manner under the assumption that consumer behavior is inflexible, i. e. cannot be adjusted in return for an adequate incentive.
Data-Driven Learning and Load Ensemble Control
Demand response (DR) programs aim to engage distributed small-scale flexible loads, such as thermostatically controllable loads (TCLs), to provide various grid support services.
Embedding Hard Physical Constraints in Convolutional Neural Networks for 3D Turbulence
Deep learning approaches have shown much promise for physical sciences, especially in dimensionality reduction and compression of large datasets.
Wavelet-Powered Neural Networks for Turbulence
One of the fundamental driving phenomena for applications in engineering, earth sciences and climate is fluid turbulence.
Embedding Hard Physical Constraints in Neural Network Coarse-Graining of 3D Turbulence
In the recent years, deep learning approaches have shown much promise in modeling complex systems in the physical sciences.
Computational Physics
Tractable Minor-free Generalization of Planar Zero-field Ising Models
We present a new family of zero-field Ising models over $N$ binary variables/spins obtained by consecutive "gluing" of planar and $O(1)$-sized components and subsets of at most three vertices into a tree.
A New Family of Tractable Ising Models
To illustrate the utility of the new family of tractable graphical models, we first build an $O(N^{3/2})$ algorithm for inference and sampling of the K5-minor-free zero-field Ising models - an extension of the planar zero-field Ising models - which is neither genus- nor treewidth-bounded.
Data Structures and Algorithms Statistical Mechanics Data Analysis, Statistics and Probability Computation
Compressed Convolutional LSTM: An Efficient Deep Learning framework to Model High Fidelity 3D Turbulence
High-fidelity modeling of turbulent flows is one of the major challenges in computational physics, with diverse applications in engineering, earth sciences and astrophysics, among many others.
Fluid Dynamics Chaotic Dynamics Computational Physics
Learning a Generator Model from Terminal Bus Data
In this work we investigate approaches to reconstruct generator models from measurements available at the generator terminal bus using machine learning (ML) techniques.
Inference and Sampling of $K_{33}$-free Ising Models
We call an Ising model tractable when it is possible to compute its partition function value (statistical inference) in polynomial time.
Gauges, Loops, and Polynomials for Partition Functions of Graphical Models
We show that the Gauge Function has a natural polynomial representation in terms of gauges/variables associated with edges of the multi-graph.
From Deep to Physics-Informed Learning of Turbulence: Diagnostics
We describe tests validating progress made toward acceleration and automation of hydrodynamic codes in the regime of developed turbulence by three Deep Learning (DL) Neural Network (NN) schemes trained on Direct Numerical Simulations of turbulence.
Real-time Faulted Line Localization and PMU Placement in Power Systems through Convolutional Neural Networks
Diverse fault types, fast re-closures, and complicated transient states after a fault event make real-time fault location in power grids challenging.
Topology Estimation using Graphical Models in Multi-Phase Power Distribution Grids
Distribution grid is the medium and low voltage part of a large power system.
Bucket Renormalization for Approximate Inference
Probabilistic graphical models are a key tool in machine learning applications.
Gauged Mini-Bucket Elimination for Approximate Inference
Recently, so-called gauge transformations were used to improve variational lower bounds on $Z$.
Online Learning of Power Transmission Dynamics
We consider the problem of reconstructing the dynamic state matrix of transmission power grids from time-stamped PMU measurements in the regime of ambient fluctuations.
Belief Propagation Min-Sum Algorithm for Generalized Min-Cost Network Flow
Belief Propagation algorithms are instruments used broadly to solve graphical model optimization and statistical inference problems.
Topology Estimation in Bulk Power Grids: Guarantees on Exact Recovery
For grids that include cycles of length three, we provide sufficient conditions that ensure existence of algorithms for exact reconstruction.
Gauging Variational Inference
Computing partition function is the most important statistical inference task arising in applications of Graphical Models (GM).
Optimal structure and parameter learning of Ising models
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.
Synthesis of MCMC and Belief Propagation
In this paper, we introduce MCMC algorithms correcting the approximation error of BP, i. e., we provide a way to compensate for BP errors via a consecutive BP-aware MCMC.
Graphical Models for Optimal Power Flow
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.
MCMC assisted by Belief Propagation
Furthermore, we also design an efficient rejection-free MCMC scheme for approximating the full series.
Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models
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.
Minimum Weight Perfect Matching via Blossom Belief Propagation
Max-product Belief Propagation (BP) is a popular message-passing algorithm for computing a Maximum-A-Posteriori (MAP) assignment over a distribution represented by a Graphical Model (GM).
Learning Planar Ising Models
Inference and learning of graphical models are both well-studied problems in statistics and machine learning that have found many applications in science and engineering.
Approximate inference on planar graphs using Loop Calculus and Belief Propagation
We introduce novel results for approximate inference on planar graphical models using the loop calculus framework.
Loop Calculus and Bootstrap-Belief Propagation for Perfect Matchings on Arbitrary Graphs
This manuscript discusses computation of the Partition Function (PF) and the Minimum Weight Perfect Matching (MWPM) on arbitrary, non-bipartite graphs.
Belief Propagation for Linear Programming
For this class of problems, MAP inference can be stated as an integer LP with an LP relaxation that coincides with minimization of the BFE at ``zero temperature".
