Cryostructuring of Polymeric Systems †: Application of Deep Neural Networks for the Classification of Structural Features Peculiar to Macroporous Poly(vinyl alcohol) Cryogels Prepared without and with the Additives of Chaotropes or Kosmotropes

Macroporous poly(vinyl alcohol) cryogels (PVACGs) are physical gels formed via cryogenic processing of polymer solutions. The properties of PVACGs depend on many factors: the characteristics and concentration of PVA, the absence or presence of foreign solutes, and the freezing-thawing conditions. These factors also affect the macroporous morphology of PVACGs, their total porosity, pore size and size distribution, etc. In this respect, there is the problem with developing a scientifically-grounded classification of the morphological features inherent in various PVACGs. In this study PVA cryogels have been prepared at different temperatures when the initial polymer solutions contained chaotropic or kosmotropic additives. After the completion of gelation, the rigidity and heat endurance of the resultant PVACGs were evaluated, and their macroporous structure was investigated using optical microscopy. The images obtained were treated mathematically, and deep neural networks were used for the classification of these images. Training and test sets were used for their classification. The results of this classification for the specific deep neural network architecture are presented, and the morphometric parameters of the macroporous structure are discussed. It was found that deep neural networks allow us to reliably classify the type of additive or its absence when using a combined dataset.

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Survey and Evaluation of Neural 3D Shape Classification Approaches

10.36227/techrxiv.14447250 ◽

2021 ◽

Author(s):

Martin Mirbauer ◽

Miroslav Krabec ◽

Jaroslav Křivánek ◽

Elena Šikudová

Keyword(s):

Neural Networks ◽

Network Architecture ◽

Deep Neural Networks ◽

Shape Classification ◽

3D Shape ◽

3D Objects ◽

Multiple Neural Networks ◽

Survey And Evaluation ◽

Selection Of

<div> <div> <div> <p>Classification of 3D objects – the selection of a category in which each object belongs – is of great interest in the field of machine learning. Numerous researchers use deep neural networks to address this problem, altering the network architecture and representation of the 3D shape used as an input. To investigate the effectiveness of their approaches, we conduct an extensive survey of existing methods and identify common ideas by which we categorize them into a taxonomy. Second, we evaluate 11 selected classification networks on three 3D object datasets, extending the evaluation to a larger dataset on which most of the selected approaches have not been tested yet. For this, we provide a framework for converting shapes from common 3D mesh formats into formats native to each network, and for training and evaluating different classification approaches on this data. Despite being generally unable to reach the accuracies reported in the original papers, we can compare the relative performance of the approaches as well as their performance when changing datasets as the only variable to provide valuable insights into performance on different kinds of data. We make our code available to simplify running training experiments with multiple neural networks with different prerequisites. </p> </div> </div> </div>

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Survey and Evaluation of Neural 3D Shape Classification Approaches

10.36227/techrxiv.14447250.v1 ◽

2021 ◽

Author(s):

Martin Mirbauer ◽

Miroslav Krabec ◽

Jaroslav Křivánek ◽

Elena Šikudová

Keyword(s):

Neural Networks ◽

Network Architecture ◽

Deep Neural Networks ◽

Shape Classification ◽

3D Shape ◽

3D Objects ◽

Multiple Neural Networks ◽

Survey And Evaluation ◽

Selection Of

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Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

Journal of Fluid Mechanics ◽

10.1017/jfm.2016.615 ◽

2016 ◽

Vol 807 ◽

pp. 155-166 ◽

Cited By ~ 274

Author(s):

Julia Ling ◽

Andrew Kurzawski ◽

Jeremy Templeton

Keyword(s):

Neural Network ◽

Neural Networks ◽

Reynolds Stress ◽

Network Architecture ◽

Eddy Viscosity ◽

Deep Neural Networks ◽

Test Cases ◽

Neural Network Architecture ◽

Stress Anisotropy ◽

Anisotropy Tensor

There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS) turbulence models that are informed by and can represent a richer set of turbulence physics. This paper presents a method of using deep neural networks to learn a model for the Reynolds stress anisotropy tensor from high-fidelity simulation data. A novel neural network architecture is proposed which uses a multiplicative layer with an invariant tensor basis to embed Galilean invariance into the predicted anisotropy tensor. It is demonstrated that this neural network architecture provides improved prediction accuracy compared with a generic neural network architecture that does not embed this invariance property. The Reynolds stress anisotropy predictions of this invariant neural network are propagated through to the velocity field for two test cases. For both test cases, significant improvement versus baseline RANS linear eddy viscosity and nonlinear eddy viscosity models is demonstrated.

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Inclusion of Multiple Cycling of the Potential into Deep Neural Network Classification of Voltammetric Reaction Mechanisms

Faraday Discussions ◽

10.1039/d1fd00050k ◽

2021 ◽

Author(s):

Luke Gundry ◽

Gareth Kennedy ◽

Alan Bond ◽

Jie Zhang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Reaction Mechanisms ◽

Deep Neural Network ◽

Deep Neural Networks ◽

Neural Network Classification ◽

Initial Cycle

The use of Deep Neural Networks (DNNs) for the classification of electrochemical mechanisms based on training with simulations of the initial cycle of potential have been reported. In this paper,...

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