877 research outputs found
Parametrization of stochastic inputs using generative adversarial networks with application in geology
We investigate artificial neural networks as a parametrization tool for
stochastic inputs in numerical simulations. We address parametrization from the
point of view of emulating the data generating process, instead of explicitly
constructing a parametric form to preserve predefined statistics of the data.
This is done by training a neural network to generate samples from the data
distribution using a recent deep learning technique called generative
adversarial networks. By emulating the data generating process, the relevant
statistics of the data are replicated. The method is assessed in subsurface
flow problems, where effective parametrization of underground properties such
as permeability is important due to the high dimensionality and presence of
high spatial correlations. We experiment with realizations of binary
channelized subsurface permeability and perform uncertainty quantification and
parameter estimation. Results show that the parametrization using generative
adversarial networks is very effective in preserving visual realism as well as
high order statistics of the flow responses, while achieving a dimensionality
reduction of two orders of magnitude
Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer
We present a study for the generation of events from a physical process with
deep generative models. The simulation of physical processes requires not only
the production of physical events, but also to ensure these events occur with
the correct frequencies. We investigate the feasibility of learning the event
generation and the frequency of occurrence with Generative Adversarial Networks
(GANs) and Variational Autoencoders (VAEs) to produce events like Monte Carlo
generators. We study three processes: a simple two-body decay, the processes
and including the decay of the top
quarks and a simulation of the detector response. We find that the tested GAN
architectures and the standard VAE are not able to learn the distributions
precisely. By buffering density information of encoded Monte Carlo events given
the encoder of a VAE we are able to construct a prior for the sampling of new
events from the decoder that yields distributions that are in very good
agreement with real Monte Carlo events and are generated several orders of
magnitude faster. Applications of this work include generic density estimation
and sampling, targeted event generation via a principal component analysis of
encoded ground truth data, anomaly detection and more efficient importance
sampling, e.g. for the phase space integration of matrix elements in quantum
field theories.Comment: 24 pages, 10 figure
Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation
Generative models for 3D geometric data arise in many important applications
in 3D computer vision and graphics. In this paper, we focus on 3D deformable
shapes that share a common topological structure, such as human faces and
bodies. Morphable Models and their variants, despite their linear formulation,
have been widely used for shape representation, while most of the recently
proposed nonlinear approaches resort to intermediate representations, such as
3D voxel grids or 2D views. In this work, we introduce a novel graph
convolutional operator, acting directly on the 3D mesh, that explicitly models
the inductive bias of the fixed underlying graph. This is achieved by enforcing
consistent local orderings of the vertices of the graph, through the spiral
operator, thus breaking the permutation invariance property that is adopted by
all the prior work on Graph Neural Networks. Our operator comes by construction
with desirable properties (anisotropic, topology-aware, lightweight,
easy-to-optimise), and by using it as a building block for traditional deep
generative architectures, we demonstrate state-of-the-art results on a variety
of 3D shape datasets compared to the linear Morphable Model and other graph
convolutional operators.Comment: to appear at ICCV 201
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