Promising directions of machine learning for partial differential equations
Partial differential equations (PDEs) are among the most universal and parsimonious
descriptions of natural physical laws, capturing a rich variety of phenomenology and …
descriptions of natural physical laws, capturing a rich variety of phenomenology and …
On the use of deep learning for computational imaging
Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …
and machine learning have followed parallel tracks and, during the last two decades …
PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network
Partial differential equations (PDEs) are commonly derived based on empirical
observations. However, recent advances of technology enable us to collect and store …
observations. However, recent advances of technology enable us to collect and store …
Pde-net: Learning pdes from data
Partial differential equations (PDEs) play a prominent role in many disciplines of science
and engineering. PDEs are commonly derived based on empirical observations. However …
and engineering. PDEs are commonly derived based on empirical observations. However …
Deep convolutional framelets: A general deep learning framework for inverse problems
Recently, deep learning approaches with various network architectures have achieved
significant performance improvement over existing iterative reconstruction methods in …
significant performance improvement over existing iterative reconstruction methods in …
Weighted low-rank tensor recovery for hyperspectral image restoration
Hyperspectral imaging, providing abundant spatial and spectral information simultaneously,
has attracted a lot of interest in recent years. Unfortunately, due to the hardware limitations …
has attracted a lot of interest in recent years. Unfortunately, due to the hardware limitations …
Embedding hard physical constraints in neural network coarse-graining of 3D turbulence
In the recent years, deep learning approaches have shown much promise in modeling
complex systems in the physical sciences. A major challenge in deep learning of PDEs is …
complex systems in the physical sciences. A major challenge in deep learning of PDEs is …
[HTML][HTML] Data-driven tight frame construction and image denoising
Sparsity-based regularization methods for image restoration assume that the underlying
image has a good sparse approximation under a certain system. Such a system can be a …
image has a good sparse approximation under a certain system. Such a system can be a …
[HTML][HTML] Applications of machine vision in pharmaceutical technology: A review
The goal of this paper is to give an introduction to analysis of images acquired by a digital
camera with visible illumination and to review its applications as a Process Analytical …
camera with visible illumination and to review its applications as a Process Analytical …
Projected iterative soft-thresholding algorithm for tight frames in compressed sensing magnetic resonance imaging
Compressed sensing (CS) has exhibited great potential for accelerating magnetic
resonance imaging (MRI). In CS-MRI, we want to reconstruct a high-quality image from very …
resonance imaging (MRI). In CS-MRI, we want to reconstruct a high-quality image from very …