A data-driven non-linear assimilation framework with neural networks

2020 ◽  
Author(s):  
Nishant Panda ◽  
M. Giselle Fernández-Godino ◽  
Humberto C. Godinez ◽  
Clint Dawson
Keyword(s):  
Neural Networks ◽  
Data Driven ◽  
Non Linear

Analysis of Non-Linear Activation Functions for Classification Tasks Using Convolutional Neural Networks

2019 ◽  
Vol 12 (3) ◽  
pp. 156-161 ◽  
Author(s):  
Aman Dureja ◽  
Payal Pahwa
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Primary Objective ◽  
Experimental Comparison ◽  
Activation Functions ◽  
Practical Applications ◽  
Network Activation ◽  
Non Linear ◽  
Hidden Layer

Background: In making the deep neural network, activation functions play an important role. But the choice of activation functions also affects the network in term of optimization and to retrieve the better results. Several activation functions have been introduced in machine learning for many practical applications. But which activation function should use at hidden layer of deep neural networks was not identified. Objective: The primary objective of this analysis was to describe which activation function must be used at hidden layers for deep neural networks to solve complex non-linear problems. Methods: The configuration for this comparative model was used by using the datasets of 2 classes (Cat/Dog). The number of Convolutional layer used in this network was 3 and the pooling layer was also introduced after each layer of CNN layer. The total of the dataset was divided into the two parts. The first 8000 images were mainly used for training the network and the next 2000 images were used for testing the network. Results: The experimental comparison was done by analyzing the network by taking different activation functions on each layer of CNN network. The validation error and accuracy on Cat/Dog dataset were analyzed using activation functions (ReLU, Tanh, Selu, PRelu, Elu) at number of hidden layers. Overall the Relu gave best performance with the validation loss at 25th Epoch 0.3912 and validation accuracy at 25th Epoch 0.8320. Conclusion: It is found that a CNN model with ReLU hidden layers (3 hidden layers here) gives best results and improve overall performance better in term of accuracy and speed. These advantages of ReLU in CNN at number of hidden layers are helpful to effectively and fast retrieval of images from the databases.

scholarly journals Uncertainty Due to Infectious Diseases and Stock–Bond Correlation

Econometrics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 17
Author(s):  
Konstantinos Gkillas ◽  
Christoforos Konstantatos ◽  
Costas Siriopoulos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Infectious Diseases ◽  
Granger Causality ◽  
Predictive Value ◽  
High Frequency ◽  
Causal Relation ◽  
Granger Causality Test ◽  
Causality Test ◽  
Non Linear

We study the non-linear causal relation between uncertainty-due-to-infectious-diseases and stock–bond correlation. To this end, we use high-frequency 1-min data to compute daily realized measures of correlation and jumps, and then, we employ a nonlinear Granger causality test with the use of artificial neural networks so as to investigate the predictability of this type of uncertainty on realized stock–bond correlation and jumps. Our findings reveal that uncertainty-due-to-infectious-diseases has significant predictive value on the changes of the stock–bond relation.

scholarly journals A computational framework to establish data-driven constitutive models for time- or path-dependent heterogeneous solids

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Weijian Ge ◽  
Vito L. Tagarielli
Keyword(s):  
Constitutive Models ◽  
Computational Procedure ◽  
Data Driven ◽  
Dissipated Energy ◽  
Computational Framework ◽  
Heterogeneous Solids ◽  
Non Linear ◽  
Recorded Data ◽  
Path Dependent ◽  
Computational Homogenisation

AbstractWe propose and implement a computational procedure to establish data-driven surrogate constitutive models for heterogeneous materials. We study the multiaxial response of non-linear n-phase composites via Finite Element (FE) simulations and computational homogenisation. Pseudo-random, multiaxial, non-proportional histories of macroscopic strain are imposed on volume elements of n-phase composites, subject to periodic boundary conditions, and the corresponding histories of macroscopic stresses and plastically dissipated energy are recorded. The recorded data is used to train surrogate, phenomenological constitutive models based on neural networks (NNs), and the accuracy of these models is assessed and discussed. We analyse heterogeneous composites with hyperelastic, viscoelastic or elastic–plastic local constitutive descriptions. In each of these three cases, we propose and assess optimal choices of inputs and outputs for the surrogate models and strategies for their training. We find that the proposed computational procedure can capture accurately and effectively the response of non-linear n-phase composites subject to arbitrary mechanical loading.

scholarly journals Extraction of organic chemistry grammar from unsupervised learning of chemical reactions

2021 ◽  
Vol 7 (15) ◽  
pp. eabe4166
Author(s):  
Philippe Schwaller ◽  
Benjamin Hoover ◽  
Jean-Louis Reymond ◽  
Hendrik Strobelt ◽  
Teodoro Laino
Keyword(s):  
Organic Chemistry ◽  
Neural Networks ◽  
Chemical Synthesis ◽  
Unsupervised Learning ◽  
Chemical Reactions ◽  
Data Driven ◽  
Experimental Task ◽  
Rule Based ◽  
Atom Mapping ◽  
Mapping Information

Humans use different domain languages to represent, explore, and communicate scientific concepts. During the last few hundred years, chemists compiled the language of chemical synthesis inferring a series of “reaction rules” from knowing how atoms rearrange during a chemical transformation, a process called atom-mapping. Atom-mapping is a laborious experimental task and, when tackled with computational methods, requires continuous annotation of chemical reactions and the extension of logically consistent directives. Here, we demonstrate that Transformer Neural Networks learn atom-mapping information between products and reactants without supervision or human labeling. Using the Transformer attention weights, we build a chemically agnostic, attention-guided reaction mapper and extract coherent chemical grammar from unannotated sets of reactions. Our method shows remarkable performance in terms of accuracy and speed, even for strongly imbalanced and chemically complex reactions with nontrivial atom-mapping. It provides the missing link between data-driven and rule-based approaches for numerous chemical reaction tasks.

scholarly journals High accuracy data-driven heliostat calibration and state prediction with pretrained deep neural networks

Solar Energy ◽  
2021 ◽  
Vol 218 ◽  
pp. 48-56
Author(s):  
Max Pargmann ◽  
Daniel Maldonado Quinto ◽  
Peter Schwarzbözl ◽  
Robert Pitz-Paal
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
High Accuracy ◽  
Data Driven ◽  
State Prediction ◽  
Accuracy Data

scholarly journals Inverse design of glass structure with deep graph neural networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qi Wang ◽  
Longfei Zhang
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Configuration Space ◽  
Glass Structure ◽  
Stable State ◽  
Specific Property ◽  
Inverse Design ◽  
Data Driven ◽  
Common Belief ◽  
Graph Neural Networks

AbstractDirectly manipulating the atomic structure to achieve a specific property is a long pursuit in the field of materials. However, hindered by the disordered, non-prototypical glass structure and the complex interplay between structure and property, such inverse design is dauntingly hard for glasses. Here, combining two cutting-edge techniques, graph neural networks and swap Monte Carlo, we develop a data-driven, property-oriented inverse design route that managed to improve the plastic resistance of Cu-Zr metallic glasses in a controllable way. Swap Monte Carlo, as a sampler, effectively explores the glass landscape, and graph neural networks, with high regression accuracy in predicting the plastic resistance, serves as a decider to guide the search in configuration space. Via an unconventional strengthening mechanism, a geometrically ultra-stable yet energetically meta-stable state is unraveled, contrary to the common belief that the higher the energy, the lower the plastic resistance. This demonstrates a vast configuration space that can be easily overlooked by conventional atomistic simulations. The data-driven techniques, structural search methods and optimization algorithms consolidate to form a toolbox, paving a new way to the design of glassy materials.

Sign in / Sign up

Export Citation Format

Share Document