3,423 research outputs found
Accelerating Eulerian Fluid Simulation With Convolutional Networks
Efficient simulation of the Navier-Stokes equations for fluid flow is a long
standing problem in applied mathematics, for which state-of-the-art methods
require large compute resources. In this work, we propose a data-driven
approach that leverages the approximation power of deep-learning with the
precision of standard solvers to obtain fast and highly realistic simulations.
Our method solves the incompressible Euler equations using the standard
operator splitting method, in which a large sparse linear system with many free
parameters must be solved. We use a Convolutional Network with a highly
tailored architecture, trained using a novel unsupervised learning framework to
solve the linear system. We present real-time 2D and 3D simulations that
outperform recently proposed data-driven methods; the obtained results are
realistic and show good generalization properties.Comment: Significant revisio
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework
We propose MeshfreeFlowNet, a novel deep learning-based super-resolution
framework to generate continuous (grid-free) spatio-temporal solutions from the
low-resolution inputs. While being computationally efficient, MeshfreeFlowNet
accurately recovers the fine-scale quantities of interest. MeshfreeFlowNet
allows for: (i) the output to be sampled at all spatio-temporal resolutions,
(ii) a set of Partial Differential Equation (PDE) constraints to be imposed,
and (iii) training on fixed-size inputs on arbitrarily sized spatio-temporal
domains owing to its fully convolutional encoder. We empirically study the
performance of MeshfreeFlowNet on the task of super-resolution of turbulent
flows in the Rayleigh-Benard convection problem. Across a diverse set of
evaluation metrics, we show that MeshfreeFlowNet significantly outperforms
existing baselines. Furthermore, we provide a large scale implementation of
MeshfreeFlowNet and show that it efficiently scales across large clusters,
achieving 96.80% scaling efficiency on up to 128 GPUs and a training time of
less than 4 minutes.Comment: Supplementary Video: https://youtu.be/mjqwPch9gDo. Accepted to SC2
A web portal for Portuguese brain imaging network
Mestrado em Engenharia de Computadores e TelemáticaA Imagiologia Cerebral (IC) está na fronteira entre a neurologia,
engenharia e física. écnicas de imagens médicas multimodais, tais como
a Ressonância Magnética (MRI e fMRI) e Espectroscopia (MRS),
Tomografia Computadorizada por Emissão de Fotões/Positrões
(SPECT/PET), entre outros, são emergentes ferramentas de pesquisa
médica que pode fornecer informações valiosas para o diagnóstico de
doenças do cérebro. Eletroencefalograma de alta resolução (HR-EEG),
técnicas para sincronizar e fundir seus resultados de análise e várias
técnicas de imagem são também parte de IC.
Em Portugal, dado o facto que a maioria das áreas relacionadas com
IC (por exemplo, medicina, engenharia ou física) são assuntos de
investigação em muitos grupos de P&D, um consórcio de universidades
de Aveiro, Coimbra, Minho e Porto criou a Rede Nacional de
Imagiologia Funcional Cerebral (RNIFC). A RNIFC é uma associação
sem fins lucrativos que foi formalizada e assinada em fevereiro de 2009.
Actualmente, com o suporte de sistemas digitais para armazenar
imagens médicas, é possível partilhar dados entre essas instituições para
melhorar o diagnóstico, e permitir investigações entre a comunidade
médica de diferentes instituições.
O principal objectivo desta dissertação é descrever a implementação
dos serviços de sistemas de informação essenciais para a Brain Imaging
Network (BIN) que suportam actualmente o RNIFC acessível através do
Portal BIN, o principal ponto de entrada para a BING. O Portal BIN
permite aos pesquisadores na comunidade BING espalhadas pelo país e
no estrangeiro, quer para solicitar o acesso a instrumentos científicos ou
para recuperar os seus casos e executar as suas análises.
ABSTRACT: Brain Imaging is in the frontier between neurology, engineering and
physics. Multimodal medical imaging techniques, such as Magnetic
Resonance Imaging (MRI and fMRI) and Spectroscopy (MRS), Single
Photon/Positron Emitting Tomography (SPECT/PET) among others, are
emergent medical research tools that can provide valuable information
for diagnosis of brain diseases. High-resolution electroencephalogram
(HR-EEG), techniques for synchronizing and fuse its analysis results and
several imaging techniques are also part of BI.
In Portugal, given fact that most of the BI related areas (e.g. medical,
engineering or physics) are subjects of research in many R&D groups, a
consortium of the universities of Aveiro, Coimbra, Minho and Porto
created the National Functional Brain Imaging Network (RNIFC). The
RNIFC is a non-profitable association that was formalized and signed in
February 2009.
Currently, with the support of digital systems to store medical images,
it is possible to share data among these institutions to improve diagnosis,
and allow investigations by the medical community among different
institutions.
The main objective of this thesis is to describe the implementation of
the essential Brain Imaging Network (BIN) information systems services
that currently support the RNIFC accessible through the BIN Portal, the
main entry point for the BING. BIN Portal enables researchers in the
BING community scattered along the country and abroad either to apply
for access to the scientific instruments or to retrieve their cases and run
their analysis
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