scholarly journals Machine Learning for Conservative-to-Primitive in Relativistic Hydrodynamics

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2157
Author(s):  
Tobias Dieselhorst ◽  
William Cook ◽  
Sebastiano Bernuzzi ◽  
David Radice
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Equation Of State ◽  
Benchmark Problems ◽  
Relativistic Hydrodynamics ◽  
Root Finding ◽  
Order Of Magnitude ◽  
Speed Up ◽  
The Neural Networks ◽  
Hydrodynamics Equations

The numerical solution of relativistic hydrodynamics equations in conservative form requires root-finding algorithms that invert the conservative-to-primitive variables map. These algorithms employ the equation of state of the fluid and can be computationally demanding for applications involving sophisticated microphysics models, such as those required to calculate accurate gravitational wave signals in numerical relativity simulations of binary neutron stars. This work explores the use of machine learning methods to speed up the recovery of primitives in relativistic hydrodynamics. Artificial neural networks are trained to replace either the interpolations of a tabulated equation of state or directly the conservative-to-primitive map. The application of these neural networks to simple benchmark problems shows that both approaches improve over traditional root finders with tabular equation-of-state and multi-dimensional interpolations. In particular, the neural networks for the conservative-to-primitive map accelerate the variable recovery by more than an order of magnitude over standard methods while maintaining accuracy. Neural networks are thus an interesting option to improve the speed and robustness of relativistic hydrodynamics algorithms.

scholarly journals Prediction Skill of Extended Range 2-m Maximum Air Temperature Probabilistic Forecasts Using Machine Learning Post-Processing Methods

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 823
Author(s):  
Ting Peng ◽  
Xiefei Zhi ◽  
Yan Ji ◽  
Luying Ji ◽  
Ye Tian
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Air Temperature ◽  
Post Processing ◽  
Learning Methods ◽  
Processing Methods ◽  
Machine Learning Methods ◽  
Extended Range ◽  
The Neural Networks ◽  
Maximum Air Temperature

The extended range temperature prediction is of great importance for public health, energy and agriculture. The two machine learning methods, namely, the neural networks and natural gradient boosting (NGBoost), are applied to improve the prediction skills of the 2-m maximum air temperature with lead times of 1–35 days over East Asia based on the Environmental Modeling Center, Global Ensemble Forecast System (EMC-GEFS), under the Subseasonal Experiment (SubX) of the National Centers for Environmental Prediction (NCEP). The ensemble model output statistics (EMOS) method is conducted as the benchmark for comparison. The results show that all the post-processing methods can efficiently reduce the prediction biases and uncertainties, especially in the lead week 1–2. The two machine learning methods outperform EMOS by approximately 0.2 in terms of the continuous ranked probability score (CRPS) overall. The neural networks and NGBoost behave as the best models in more than 90% of the study area over the validation period. In our study, CRPS, which is not a common loss function in machine learning, is introduced to make probabilistic forecasting possible for traditional neural networks. Moreover, we extend the NGBoost model to atmospheric sciences of probabilistic temperature forecasting which obtains satisfying performances.

scholarly journals Neural network reconstruction of the dense matter equation of state derived from the parameters of neutron stars

2020 ◽  
Vol 642 ◽  
pp. A78 ◽  
Author(s):  
F. Morawski ◽  
M. Bejger
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Neutron Star ◽  
Equation Of State ◽  
Neutron Stars ◽  
Equations Of State ◽  
Dense Matter ◽  
Data Set ◽  
Global Parameters

Context. Neutron stars are currently studied with an rising number of electromagnetic and gravitational-wave observations, which will ultimately allow us to constrain the dense matter equation of state and understand the physical processes at work within these compact objects. Neutron star global parameters, such as the mass and radius, can be used to obtain the equation of state by directly inverting the Tolman-Oppenheimer-Volkoff equations. Here, we investigate an alternative approach to this procedure. Aims. The aim of this work is to study the application of the artificial neural networks guided by the autoencoder architecture as a method for precisely reconstructing the neutron star equation of state, using their observable parameters: masses, radii, and tidal deformabilities. In addition, we study how well the neutron star radius can be reconstructed using only the gravitational-wave observations of tidal deformability, that is, using quantities that are not related in any straightforward way. Methods. The application of an artificial neural network in the equation-of-state reconstruction exploits the non-linear potential of this machine learning model. Since each neuron in the network is basically a non-linear function, it is possible to create a complex mapping between the input sets of observations and the output equation-of-state table. Within the supervised training paradigm, we construct a few hidden-layer deep neural networks on a generated data set, consisting of a realistic equation of state for the neutron star crust connected with a piecewise relativistic polytropes dense core, with its parameters representative of state-of-the art realistic equations of state. Results. We demonstrate the performance of our machine-learning implementation with respect to the simulated cases with a varying number of observations and measurement uncertainties. Furthermore, we study the impact of the neutron star mass distributions on the results. Finally, we test the reconstruction of the equation of state trained on parametric polytropic training set using the simulated mass–radius and mass–tidal-deformability sequences based on realistic equations of state. Neural networks trained with a limited data set are capable of generalising the mapping between global parameters and equation-of-state input tables for realistic models.

The Machine Learning Embedded Method of Parameters Determination in the Constitutive Models and Potential Applications for Hydrogels

2021 ◽  
Vol 13 (01) ◽  
pp. 2150001 ◽  
Author(s):  
Shoujing Zheng ◽  
Zishun Liu
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Chemical Potential ◽  
Constitutive Models ◽  
Experimental Application ◽  
Potential Applications ◽  
The Neural Networks ◽  
Hydrogel Swelling ◽  
Embedded Method ◽  
Fitting Parameters

We propose a machine learning embedded method of parameters determination in the constitutional models of hydrogels. It is found that the developed logistic regression-like algorithm for hydrogel swelling allows us to determine the fitting parameters based on known swelling ratio and chemical potential. We also put forward the neural networks-like algorithm, which, by its own property, can converge faster as the layer deepens. We then develop neural networks-like algorithm for hydrogel under uniaxial load for experimental application purpose. Finally, we propose several machine learning embedded potential applications for hydrogels, which would provide directions for machine learning-based hydrogel research.

scholarly journals Efficient Hyperparameter Tuning with Grid Search for Text Categorization using kNN Approach with BM25 Similarity

2019 ◽  
Vol 9 (1) ◽  
pp. 160-180 ◽  
Author(s):  
Raji Ghawi ◽  
Jürgen Pfeffer
Keyword(s):  
Machine Learning ◽  
Text Categorization ◽  
Learning Algorithm ◽  
Efficient Technique ◽  
Grid Search ◽  
Order Of Magnitude ◽  
Speed Up

AbstractIn machine learning, hyperparameter tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. Several approaches have been widely adopted for hyperparameter tuning, which is typically a time consuming process. We propose an efficient technique to speed up the process of hyperparameter tuning with Grid Search. We applied this technique on text categorization using kNN algorithm with BM25 similarity, where three hyperparameters need to be tuned. Our experiments show that our proposed technique is at least an order of magnitude faster than conventional tuning.

Research on the Identification for a Nonlinear System

2011 ◽  
Vol 216 ◽  
pp. 39-44
Author(s):  
Shi Liang Lv ◽  
Jin Guo Liu ◽  
Ping Jia
Keyword(s):  
Neural Networks ◽  
Nonlinear System ◽  
Nonlinear Dynamic System ◽  
Universal Approximation ◽  
Dynamic Information ◽  
Input And Output ◽  
Learning Speed ◽  
Speed Up ◽  
The Neural Networks ◽  
Networks Structure

The characteristic of the drift error of inertial platform is a high-order nonlinear dynamic system, using the neural networks’ abilities of universal approximation of differentiable trajectory and capturing system dynamic information, this paper presents the drift error identifying project of inertial platform based on Elman networks structure. First, the drift error model of inertial platform is established, after selecting the input and output for network, momentum and alterable speed algorithm is used to speed up the network convergence. On the basis of the algorithm, the extended nonlinear node function in the hidden network does not only improve the learning speed of network, but also satisfies the need of accuracy on system identification. Through the drift error data measured on inertial platform, the training result shows that the scheme achieves satisfied identification results.

Machine Learning Techniques for Estimating Amount of Coolant Required in Shipping of Temperature Sensitive Products

2020 ◽  
Vol 10 (10) ◽  
pp. 67-70
Author(s):  
Divya Choudhary ◽  
Siripong Malasri
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Threshold Temperature ◽  
Support Vector ◽  
Temperature Sensitive ◽  
Learning Techniques ◽  
The Neural Networks

This paper implements and compares machine learning algorithms to predict the amount of coolant required during transportation of temperature sensitive products. The machine learning models use trip duration, product threshold temperature and ambient temperature as the independent variables to predict the weight of gel packs need to keep the temperature of the product below its threshold temperature value. The weight of the gel packs can be translated to number of gel packs required. Regression using Neural Networks, Support Vector Regression, Gradient Boosted Regression and Elastic Net Regression are compared. The Neural Networks based model performs the best in terms of its mean absolute error value and r-squared values. A Neural Network model is then deployed on as webservice to score allowing for client application to make rest calls to estimate gel pack weights

scholarly journals Toward accurate platform-aware performance modeling for deep neural networks

2021 ◽  
Vol 21 (1) ◽  
pp. 50-61
Author(s):  
Chuan-Chi Wang ◽  
Ying-Chiao Liao ◽  
Ming-Chang Kao ◽  
Wen-Yew Liang ◽  
Shih-Hao Hung
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Network Performance ◽  
Percentage Error ◽  
Training Time ◽  
Fine Grain ◽  
The Neural Network ◽  
Machine Learning Model ◽  
The Neural Networks ◽  
System Developer

In this paper, we provide a fine-grain machine learning-based method, PerfNetV2, which improves the accuracy of our previous work for modeling the neural network performance on a variety of GPU accelerators. Given an application, the proposed method can be used to predict the inference time and training time of the convolutional neural networks used in the application, which enables the system developer to optimize the performance by choosing the neural networks and/or incorporating the hardware accelerators to deliver satisfactory results in time. Furthermore, the proposed method is capable of predicting the performance of an unseen or non-existing device, e.g. a new GPU which has a higher operating frequency with less processor cores, but more memory capacity. This allows a system developer to quickly search the hardware design space and/or fine-tune the system configuration. Compared to the previous works, PerfNetV2 delivers more accurate results by modeling detailed host-accelerator interactions in executing the full neural networks and improving the architecture of the machine learning model used in the predictor. Our case studies show that PerfNetV2 yields a mean absolute percentage error within 13.1% on LeNet, AlexNet, and VGG16 on NVIDIA GTX-1080Ti, while the error rate on a previous work published in ICBD 2018 could be as large as 200%.

scholarly journals Modelling and Analyzing the Employees’ Engagement in Workplace using Machine learning Tools

2021 ◽  
Vol 9 (6) ◽  
pp. 243-249
Author(s):  
Mahassine BEKKARI ◽  
EL FALLAHI Abdellah
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Human Capital ◽  
New Economy ◽  
Gradient Boosting ◽  
Learning Tools ◽  
Response Variable ◽  
Complex Relation ◽  
Gradient Boosting Machine ◽  
The Neural Networks

In a new economy where immaterial capital is crucial, companies are increasingly aware of the necessity to efficiently manage human capital by optimizing its engagement in the workplace. The accession of the human capital through its engagement is an efficient leverage that leads to a real improvement of the companies’ performance. Despite the staple attention towards human resource management, and the efforts undertaken to satisfy and motivate the personnel, the issue of engagement still persists. The main objective of this paper is to study and model the relation between eight predictors and a response variable given by the employees’ engagement. We have used different models to figure out the relation between the predictors and the dependent variable after carrying out a survey of several employees from different companies. The techniques used in this paper are linear regression, ordinal logistic regression, Gradient Boosting Machine learning and neural networks. The data used in this study is the results of a questionnaire completed by 60 individuals. The results obtained show that the neural networks perform slightly the rest of models considering the training and validation error of modelling and also highlight the complex relation linking the predictors and the predicted.

scholarly journals Predicting atmospheric optical properties for radiative transfer computations using neural networks

2021 ◽  
Vol 379 (2194) ◽  
pp. 20200095
Author(s):  
Menno A. Veerman ◽  
Robert Pincus ◽  
Robin Stoffer ◽  
Caspar M. van Leeuwen ◽  
Damian Podareanu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Optical Properties ◽  
Radiative Transfer ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Climate Modelling ◽  
Atmospheric Conditions ◽  
Promising Tool ◽  
The Neural Networks

The radiative transfer equations are well known, but radiation parametrizations in atmospheric models are computationally expensive. A promising tool for accelerating parametrizations is the use of machine learning techniques. In this study, we develop a machine learning-based parametrization for the gaseous optical properties by training neural networks to emulate a modern radiation parametrization (RRTMGP). To minimize computa- tional costs, we reduce the range of atmospheric conditions for which the neural networks are applicable and use machine-specific optimized BLAS functions to accelerate matrix computations. To generate training data, we use a set of randomly perturbed atmospheric profiles and calculate optical properties using RRTMGP. Predicted optical properties are highly accurate and the resulting radiative fluxes have average errors within 0.5 W m −2 compared to RRTMGP. Our neural network-based gas optics parametrization is up to four times faster than RRTMGP, depending on the size of the neural networks. We further test the trade-off between speed and accuracy by training neural networks for the narrow range of atmospheric conditions of a single large-eddy simulation, so smaller and therefore faster networks can achieve a desired accuracy. We conclude that our machine learning-based parametrization can speed-up radiative transfer computations while retaining high accuracy. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

scholarly journals Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques

Risks ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 22 ◽  
Author(s):  
Mathias Bärtl ◽  
Simone Krummaker
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Test Performance ◽  
Machine Learning Techniques ◽  
Export Credit ◽  
Credit Insurance ◽  
Comprehensive Collection ◽  
Export Credit Insurance ◽  
Learning Techniques ◽  
Order Of Magnitude

This study evaluates four machine learning (ML) techniques (Decision Trees (DT), Random Forests (RF), Neural Networks (NN) and Probabilistic Neural Networks (PNN)) on their ability to accurately predict export credit insurance claims. Additionally, we compare the performance of the ML techniques against a simple benchmark (BM) heuristic. The analysis is based on the utilisation of a dataset provided by the Berne Union, which is the most comprehensive collection of export credit insurance data and has been used in only two scientific studies so far. All ML techniques performed relatively well in predicting whether or not claims would be incurred, and, with limitations, in predicting the order of magnitude of the claims. No satisfactory results were achieved predicting actual claim ratios. RF performed significantly better than DT, NN and PNN against all prediction tasks, and most reliably carried their validation performance forward to test performance.

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