4,012 research outputs found
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
A central problem in machine learning involves modeling complex data-sets
using highly flexible families of probability distributions in which learning,
sampling, inference, and evaluation are still analytically or computationally
tractable. Here, we develop an approach that simultaneously achieves both
flexibility and tractability. The essential idea, inspired by non-equilibrium
statistical physics, is to systematically and slowly destroy structure in a
data distribution through an iterative forward diffusion process. We then learn
a reverse diffusion process that restores structure in data, yielding a highly
flexible and tractable generative model of the data. This approach allows us to
rapidly learn, sample from, and evaluate probabilities in deep generative
models with thousands of layers or time steps, as well as to compute
conditional and posterior probabilities under the learned model. We
additionally release an open source reference implementation of the algorithm
Learning Exact Topology of a Loopy Power Grid from Ambient Dynamics
Estimation of the operational topology of the power grid is necessary for
optimal market settlement and reliable dynamic operation of the grid. This
paper presents a novel framework for topology estimation for general power
grids (loopy or radial) using time-series measurements of nodal voltage phase
angles that arise from the swing dynamics. Our learning framework utilizes
multivariate Wiener filtering to unravel the interaction between fluctuations
in voltage angles at different nodes and identifies operational edges by
considering the phase response of the elements of the multivariate Wiener
filter. The performance of our learning framework is demonstrated through
simulations on standard IEEE test cases.Comment: accepted as a short paper in ACM eEnergy 2017, Hong Kon
Turbulence model reduction by deep learning
A central problem of turbulence theory is to produce a predictive model for
turbulent fluxes. These have profound implications for virtually all aspects of
the turbulence dynamics. In magnetic confinement devices, drift-wave turbulence
produces anomalous fluxes via cross-correlations between fluctuations. In this
work, we introduce a new, data-driven method for parameterizing these fluxes.
The method uses deep supervised learning to infer a reduced mean-field model
from a set of numerical simulations. We apply the method to a simple drift-wave
turbulence system and find a significant new effect which couples the particle
flux to the local \emph{gradient} of vorticity. Notably, here, this effect is
much stronger than the oft-invoked shear suppression effect. We also recover
the result via a simple calculation. The vorticity gradient effect tends to
modulate the density profile. In addition, our method recovers a model for
spontaneous zonal flow generation by negative viscosity, stabilized by
nonlinear and hyperviscous terms. We highlight the important role of symmetry
to implementation of the new method.Comment: To be published in Phys. Rev. E Rap. Comm. 6 pages, 7 figure
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