scholarly journals Data-driven approximations to the bridge function yield improved closures for the Ornstein–Zernike equation

Soft Matter ◽  
2021 ◽  
Author(s):  
Rhys E. A. Goodall ◽  
Alpha A. Lee
Keyword(s):  
Deep Learning ◽  
Condensed Phase ◽  
Soft Matter ◽  
Data Driven ◽  
Interaction Potentials ◽  
Bridge Function ◽  
Phase Structures

A central challenge for soft matter is determining interaction potentials that give rise to observed condensed phase structures. Here we tackle this problem by combining the power of Deep Learning with the physics of the Ornstein–Zernike equation.

scholarly journals High-Resolution ISAR Imaging and Autofocusing via 2D-ADMM-Net

2021 ◽  
Vol 13 (12) ◽  
pp. 2326
Author(s):  
Xiaoyong Li ◽  
Xueru Bai ◽  
Feng Zhou
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Phase Error ◽  
Random Phase ◽  
Back Propagation ◽  
Estimation Method ◽  
Data Driven ◽  
Reconstruction Performance ◽  
Imaging Results ◽  
Isar Imaging

A deep-learning architecture, dubbed as the 2D-ADMM-Net (2D-ADN), is proposed in this article. It provides effective high-resolution 2D inverse synthetic aperture radar (ISAR) imaging under scenarios of low SNRs and incomplete data, by combining model-based sparse reconstruction and data-driven deep learning. Firstly, mapping from ISAR images to their corresponding echoes in the wavenumber domain is derived. Then, a 2D alternating direction method of multipliers (ADMM) is unrolled and generalized to a deep network, where all adjustable parameters in the reconstruction layers, nonlinear transform layers, and multiplier update layers are learned by an end-to-end training through back-propagation. Since the optimal parameters of each layer are learned separately, 2D-ADN exhibits more representation flexibility and preferable reconstruction performance than model-driven methods. Simultaneously, it is able to better facilitate ISAR imaging with limited training samples than data-driven methods owing to its simple structure and small number of adjustable parameters. Additionally, benefiting from the good performance of 2D-ADN, a random phase error estimation method is proposed, through which well-focused imaging can be acquired. It is demonstrated by experiments that although trained by only a few simulated images, the 2D-ADN shows good adaptability to measured data and favorable imaging results with a clear background can be obtained in a short time.

scholarly journals Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2778 ◽  
Author(s):  
Mohsen Azimi ◽  
Armin Eslamlou ◽  
Gokhan Pekcan
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Speed ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Driven ◽  
Structural Health ◽  
Promising Tool ◽  
Significant Attention

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

scholarly journals Data-Driven Intelligent Efficient Synaptic Storage for Deep Learning

2017 ◽  
Vol 64 (12) ◽  
pp. 1412-1416 ◽  
Author(s):  
Jonathon Edstrom ◽  
Yifu Gong ◽  
Dongliang Chen ◽  
Jinhui Wang ◽  
Na Gong
Keyword(s):  
Deep Learning ◽  
Data Driven

A data-driven XRD analysis protocol for phase identification and phase-fraction prediction of multiphase inorganic compounds

2021 ◽  
Author(s):  
Jin-Woong Lee ◽  
Woon Bae Park ◽  
Minseuk Kim ◽  
Satendra Pal Singh ◽  
Myoungho Pyo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Inorganic Compounds ◽  
Xrd Analysis ◽  
Data Driven ◽  
Phase Fraction ◽  
Phase Identification ◽  
Constituent Phase ◽  
Analysis Protocol

Deep learning (DL) models trained with synthetic XRD data have never accomplished a satisfactory quantitative XRD analysis for the exact prediction of a constituent-phase fraction in unknown multiphase inorganic compounds,...

Data-driven method for training data selection for deep learning

2021 ◽  
Author(s):  
C. Lacombe ◽  
I. Hammoud ◽  
J. Messud ◽  
H. Peng ◽  
T. Lesieur ◽  
...  
Keyword(s):  
Deep Learning ◽  
Training Data ◽  
Data Selection ◽  
Data Driven ◽  
Selection For ◽  
Training Data Selection
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