2,767 research outputs found
GRIDS-Net: Inverse shape design and identification of scatterers via geometric regularization and physics-embedded deep learning
This study presents a deep learning based methodology for both remote sensing
and design of acoustic scatterers. The ability to determine the shape of a
scatterer, either in the context of material design or sensing, plays a
critical role in many practical engineering problems. This class of inverse
problems is extremely challenging due to their high-dimensional, nonlinear, and
ill-posed nature. To overcome these technical hurdles, we introduce a geometric
regularization approach for deep neural networks (DNN) based on non-uniform
rational B-splines (NURBS) and capable of predicting complex 2D scatterer
geometries in a parsimonious dimensional representation. Then, this geometric
regularization is combined with physics-embedded learning and integrated within
a robust convolutional autoencoder (CAE) architecture to accurately predict the
shape of 2D scatterers in the context of identification and inverse design
problems. An extensive numerical study is presented in order to showcase the
remarkable ability of this approach to handle complex scatterer geometries
while generating physically-consistent acoustic fields. The study also assesses
and contrasts the role played by the (weakly) embedded physics in the
convergence of the DNN predictions to a physically consistent inverse design.Comment: 23 pages of main text, 10 figure
TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions
Although deep learning approaches have had tremendous success in image, video
and audio processing, computer vision, and speech recognition, their
applications to three-dimensional (3D) biomolecular structural data sets have
been hindered by the entangled geometric complexity and biological complexity.
We introduce topology, i.e., element specific persistent homology (ESPH), to
untangle geometric complexity and biological complexity. ESPH represents 3D
complex geometry by one-dimensional (1D) topological invariants and retains
crucial biological information via a multichannel image representation. It is
able to reveal hidden structure-function relationships in biomolecules. We
further integrate ESPH and convolutional neural networks to construct a
multichannel topological neural network (TopologyNet) for the predictions of
protein-ligand binding affinities and protein stability changes upon mutation.
To overcome the limitations to deep learning arising from small and noisy
training sets, we present a multitask topological convolutional neural network
(MT-TCNN). We demonstrate that the present TopologyNet architectures outperform
other state-of-the-art methods in the predictions of protein-ligand binding
affinities, globular protein mutation impacts, and membrane protein mutation
impacts.Comment: 20 pages, 8 figures, 5 table
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
EMG-to-Speech: Direct Generation of Speech from Facial Electromyographic Signals
The general objective of this work is the design, implementation, improvement and evaluation of a system that uses surface electromyographic (EMG) signals and directly synthesizes an audible speech output: EMG-to-speech
Proceedings, MSVSCC 2018
Proceedings of the 12th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2018 at VMASC in Suffolk, Virginia. 155 pp
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