scholarly journals Uncertainty Quantification of Deep Neural Network-Based Turbulence Model for Reactor Transient Analysis

2021 ◽  
Author(s):  
Yang Liu ◽  
Rui Hu ◽  
Prasanna Balaprakash
Keyword(s):  
Neural Network ◽  
Computational Cost ◽  
Computationally Efficient ◽  
Bayesian Neural Network ◽  
Convolutional Network ◽  
Closure Relation ◽  
Sophisticated Method ◽  
Linear Relationships ◽  
Uncertainty Estimates ◽  
Low Computational Cost

Abstract Deep neural networks (DNNs) have demonstrated good performance in learning highly non-linear relationships in large datasets, thus have been considered as a promising surrogate modeling tool for parametric partial differential equations (PDEs). On the other hand, quantifying the predictive uncertainty in DNNs is still a challenging problem. The Bayesian neural network (BNN), a sophisticated method assuming the weights of the DNNs follow certain uncertainty distributions, is considered as a state-of-the-art method for the UQ of DNNs. However, the method is too computationally expensive to be used in complicated DNN architectures. In this work, we utilized two more methods for the UQ of complicated DNNs, i.e. Monte Carlo dropout and deep ensemble. Both methods are computationally efficient and scalable compared to BNN. We applied these two methods to a densely connected convolutional network, which is developed and trained as a coarse-mesh turbulence closure relation for reactor safety analysis. In comparison, the corresponding BNN with the same architecture is also developed and trained. The computational cost and uncertainty evaluation performance of these three UQ methods are comprehensively investigated. It is found that the deep ensemble method is able to produce reasonable uncertainty estimates with good scalability and relatively low computational cost compared to BNN.

Web application to support evidence of individual emotional impact evoked by COVID-19 pandemic restrictions (Preprint)

2021 ◽  
Author(s):  
Hugo Mitre-Hernandez ◽  
Rodolfo Ferro-Perez ◽  
Francisco Gonzalez-Hernandez
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
Neural Networks ◽  
Emotion Recognition ◽  
Web Application ◽  
Deep Neural Network ◽  
Data Transfer ◽  
Computational Cost ◽  
Low Computational Cost ◽  
The Web

BACKGROUND Mental health effects during COVID-19 quarantine need to be handled because patients, relatives, and healthcare workers are living with negative emotional behaviors. The clinical disorders of depression and anxiety are evoking anger, fear, sadness, disgust, and reducing happiness. Therefore, track emotions with the help of psychologists on online consultations –to reduce the risk of contagion– will go a long way in assisting with mental health. The human micro-expressions can describe genuine emotions of people and can be captured by Deep Neural Networks (DNNs) models. But the challenge is to implement it under the poor performance of a part of society's computers and the low speed of internet connection. OBJECTIVE This study aimed to create a useful and usable web application to record emotions in a patient’s card in real-time, achieving a small data transfer, and a Convolutional Neural Networks (CNN) model with a low computational cost. METHODS To validate the low computational cost premise, firstly, we compare DNN architectures results, collecting the floating-point operations per second (FLOPS), the Number of Parameters (NP) and accuracy from the MobileNet, PeleeNet, Extended Deep Neural Network (EDNN), Inception- Based Deep Neural Network (IDNN) and our proposed Residual mobile-based Network (ResmoNet) model. Secondly, we compare the trained models' results in terms of Main Memory Utilization (MMU) and Response Time to complete the Emotion recognition (RTE). Finally, we design a data transfer that includes the raw data of emotions and the basic text information of the patient. The web application was evaluated with the System Usability Scale (SUS) and a utility questionnaire by psychologists and psychiatrists (experts). RESULTS All CNN models were set up using 150 epochs for training and testing comparing the results for each variable in ResmoNet with the best model. It was obtained that ResmoNet has 115,976 NP less than MobileNet, 243,901 FLOPS less than MobileNet, and 5% less accuracy than EDNN (95%). Moreover, ResmoNet used less MMU than any model, only EDNN overcomes ResmoNet in 0.01 seconds for RTE. Finally, with our model, we develop a web application to collect emotions in real-time during a psychological consultation. For data transfer, the patient’s card and raw emotional data have 2 kb with a UTF-8 encoding approximately. Finally, according to the experts, the web application has good usability (73.8 of 100) and utility (3.94 of 5). CONCLUSIONS A usable and useful web application for psychologists and psychiatrists is presented. This tool includes an efficient and light facial emotion recognition model. Its purpose is to be a complementary tool for diagnostic processes.

scholarly journals Assessing Hydrologic Impact of Climate Change with Uncertainty Estimates: Bayesian Neural Network Approach

2010 ◽  
Vol 11 (2) ◽  
pp. 482-495 ◽  
Author(s):  
Mohammad Sajjad Khan ◽  
Paulin Coulibaly
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Rainfall Runoff ◽  
Bayesian Neural Network ◽  
Uncertainty Estimates ◽  
Change Impact ◽  
Climate Change Impact Study ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Different Sources

Abstract A major challenge in assessing the hydrologic effect of climate change remains the estimation of uncertainties associated with different sources, such as the global climate models, emission scenarios, downscaling methods, and hydrologic models. There is a demand for an efficient and easy-to-use rainfall–runoff modeling tool that can capture the different sources of uncertainties to generate future flow simulations that can be used for decision making. To manage the large range of uncertainties in the climate change impact study on water resources, a neural network–based rainfall–runoff model—namely, Bayesian neural network (BNN)—is proposed. The BNN model is used with Canadian Centre for Climate Modelling and Analysis Coupled GCM, versions 1 and 2 (CGCM1 and CGCM2, respectively) with two emission scenarios, Intergovernmental Panel on Climate Change (IPCC) IS92a and Special Report on Emissions Scenarios (SRES) B2. One widely used statistical downscaling model (SDSM) is used in the analysis. The study is undertaken to simulate daily river flow and daily reservoir inflow in the Serpent and the Chute-du-Diable watersheds, respectively, in northeastern Canada. It is found that the uncertainty bands of the mean ensemble flow (i.e., flow simulated using the mean of the ensemble members of downscaled meteorological variables) is able to mostly encompass all other flows simulated with various individual downscaled meteorological ensemble members whichever CGCM or emission scenario is used. In addition, the uncertainty bands are also able to typically encompass most of the flows simulated with another rainfall–runoff model, namely, Hydrologiska Byråns Vattenbalansavdelning (HBV). The study results suggest that the BNN model could be used as an effective hydrological modeling tool in assessing the hydrologic effect of climate change with uncertainty estimates in the form of confidence intervals. It could be a good alternative method where resources are not available to implement the general multimodel ensembles approach. The BNN approach makes the climate change impact study on water resources with uncertainty estimate relatively simple, cost effective, and time efficient.

Fusing model ensembles and observations together with Bayesian neural networks

2021 ◽  
Author(s):  
Matt Amos ◽  
Ushnish Sengupta ◽  
Scott Hosking ◽  
Paul Young
Keyword(s):  
Neural Network ◽  
Total Ozone ◽  
Climate Models ◽  
Model Performance ◽  
Physical Models ◽  
Bayesian Neural Network ◽  
Total Ozone Column ◽  
Uncertainty Estimates ◽  
Model Ensembles ◽  
Ozone Column

<p>To fuse together output from ensembles of climate models with observations, we have developed a custom Bayesian neural network that produces more accurate and uncertainty aware projections.</p><p>Ensembles of physical models are typically used to increase the accuracy of projections and quantify projective uncertainties. However, few methods for combining ensemble output consider differing model performance or similarity between models. Current weighting strategies that do, typically assume model weights are invariant in time and space though this is rarely the case in models.</p><p>Our Bayesian neural network infers spatiotemporally varying model weights, bias and uncertainty to capture that some regions or seasons are better simulated in certain models. The Bayesian neural network learns how to optimally combine multiple models in order to replicate observations and can also be used to infill gaps in historic observations. In regions of sparse observations, it infers from both the surrounding data and similar physical conditions. Although we are using a typically black box technique, the attribution of model weights and bias maintains interpretability.</p><p>We demonstrate the utility of the Bayesian neural network by using it to combine multiple chemistry climate models to produce continuous historic predictions of the total ozone column (1980-2010) and projections of total ozone column for the 21st century, both with principled uncertainty estimates. Rigorous validation shows that our Bayesian neural network predictions outperform standard methods of assimilating models.</p>

A Neural-Network-Assisted Optimization Framework and Its Use for Optimum-Parameter Identification

2006 ◽  
pp. 221-235 ◽  
Author(s):  
Tapabrata Ray
Keyword(s):  
Neural Network ◽  
Design Optimization ◽  
Optimum Parameter ◽  
Optimization Problems ◽  
Computational Cost ◽  
Optimum Process ◽  
Optimization Framework ◽  
Computationally Expensive ◽  
Expensive Process ◽  
Low Computational Cost

Surrogate-assisted optimization frameworks are of great use in solving practical computationally expensive process-design-optimization problems. In this chapter, a framework for design optimization is introduced that makes use of neural-network-based surrogates in lieu of actual analysis to arrive at optimum process parameters. The performance of the algorithm is studied using a number of mathematical benchmarks to instill confidence on its performance before reporting the results of a springback minimization problem. The results clearly indicate that the framework is able to report optimum designs with a substantially low computational cost while maintaining an acceptable level of accuracy.

One-dimensional deep learning inversion of electromagnetic induction data using convolutional neural network

2020 ◽  
Vol 222 (1) ◽  
pp. 247-259 ◽  
Author(s):  
Davood Moghadas
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Electromagnetic Induction ◽  
Computational Cost ◽  
Single Step ◽  
Accurate Estimation ◽  
Well Performance ◽  
Convolutional Network ◽  
Data Set

SUMMARY Conventional geophysical inversion techniques suffer from several limitations including computational cost, nonlinearity, non-uniqueness and dimensionality of the inverse problem. Successful inversion of geophysical data has been a major challenge for decades. Here, a novel approach based on deep learning (DL) inversion via convolutional neural network (CNN) is proposed to instantaneously estimate subsurface electrical conductivity (σ) layering from electromagnetic induction (EMI) data. In this respect, a fully convolutional network was trained on a large synthetic data set generated based on 1-D EMI forward model. The accuracy of the proposed approach was examined using several synthetic scenarios. Moreover, the trained network was used to find subsurface electromagnetic conductivity images (EMCIs) from EMI data measured along two transects from Chicken Creek catchment (Brandenburg, Germany). Dipole–dipole electrical resistivity tomography data were measured as well to obtain reference subsurface σ distributions down to a 6 m depth. The inversely estimated models were juxtaposed and compared with their counterparts obtained from a spatially constrained deterministic algorithm as a standard code. Theoretical simulations demonstrated a well performance of the algorithm even in the presence of noise in data. Moreover, application of the DL inversion for subsurface imaging from Chicken Creek catchment manifested the accuracy and robustness of the proposed approach for EMI inversion. This approach returns subsurface σ distribution directly from EMI data in a single step without any iterations. The proposed strategy simplifies considerably EMI inversion and allows for rapid and accurate estimation of subsurface EMCI from multiconfiguration EMI data.

scholarly journals A continuous vertically resolved ozone dataset from the fusion of chemistry climate models with observations using a Bayesian neural network

2021 ◽  
Author(s):  
Matt Amos ◽  
Ushnish Sengupta ◽  
Paul Young ◽  
J. Hosking
Keyword(s):  
Neural Network ◽  
Climate Models ◽  
Stratospheric Ozone ◽  
Polar Regions ◽  
Bayesian Neural Network ◽  
Learning Framework ◽  
Continuous Coverage ◽  
Rigorous Testing ◽  
Uncertainty Estimates ◽  
Continuous Record

Continuous historic datasets of vertically resolved stratospheric ozone, support the case for ozone recovery, are necessary for the running of offline models and increase understanding of the impacts of ozone on the wider atmospheric system. Vertically resolved ozone datasets are typically constructed from multiple satellite, sonde and ground-based measurements that do not provide continuous coverage. As a result, several methods have been used to infill these gaps, most commonly relying on regression against observed time series. However, these existing methods either provide low accuracy infilling especially over polar regions, unphysical extrapolation, or an incomplete estimation of uncertainty. To address these methodological shortcomings we used and further developed an infilling framework that fuses observations with output from an ensemble of chemistry-climate models within a Bayesian neural network. We used this deep learning framework to produce a continuous record of vertically resolved ozone with uncertainty estimates. Under rigorous testing the infilling framework extrapolated and interpolated skillfully and maintained realistic interannual variability due to the inclusion of physically and chemically realistic models. This framework and the ozone dataset it produced, enables a more thorough investigation of vertically resolved trends throughout the atmosphere.

scholarly journals Computationally Efficient Wildfire Detection Method Using a Deep Convolutional Network Pruned via Fourier Analysis

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2891 ◽  
Author(s):  
Hongyi Pan ◽  
Diaa Badawi ◽  
Ahmet Enis Cetin
Keyword(s):  
Neural Network ◽  
Detection Method ◽  
Fire Detection ◽  
Computationally Efficient ◽  
Convolutional Network ◽  
Video Clips ◽  
Wild Fire ◽  
The Neural Network ◽  
Analysis Strategy ◽  
Regular Network

In this paper, we propose a deep convolutional neural network for camera based wildfire detection. We train the neural network via transfer learning and use window based analysis strategy to increase the fire detection rate. To achieve computational efficiency, we calculate frequency response of the kernels in convolutional and dense layers and eliminate those filters with low energy impulse response. Moreover, to reduce the storage for edge devices, we compare the convolutional kernels in Fourier domain and discard similar filters using the cosine similarity measure in the frequency domain. We test the performance of the neural network with a variety of wildfire video clips and the pruned system performs as good as the regular network in daytime wild fire detection, and it also works well on some night wild fire video clips.

scholarly journals Traffic Monitoring System Based on Deep Learning and Seismometer Data

2021 ◽  
Vol 11 (10) ◽  
pp. 4590
Author(s):  
Ahmad Bahaa Ahmad ◽  
Takeshi Tsuji
Keyword(s):  
Neural Network ◽  
Seismic Data ◽  
Computational Cost ◽  
Vertical Component ◽  
Traffic Monitoring ◽  
Vehicle Classification ◽  
Magnetic Characteristics ◽  
Seismic Records ◽  
The Neural Networks ◽  
Low Computational Cost

Currently, vehicle classification in roadway-based techniques depends mainly on photos/videos collected by an over-roadway camera or on the magnetic characteristics of vehicles. However, camera-based techniques are criticized for potentially violating the privacy of vehicle occupants and exposing their identity, and vehicles can evade detection when they are obscured by larger vehicles. Here, we evaluate methods of identifying and classifying vehicles on the basis of seismic data. Vehicle identification from seismic signals is considered a difficult task because of interference by various noise. By analogy with techniques used in speech recognition, we used different artificial intelligence techniques to extract features of three, different-sized vehicles (buses, cars, motorcycles) and seismic noise. We investigated the application of a deep neural network (DNN), a convolutional neural network (CNN), and a recurrent neural network (RNN) to classify vehicles on the basis of vertical-component seismic data recorded by geophones. The neural networks were trained on 5580 unprocessed seismic records and achieved excellent training accuracy (99%). They were also tested on large datasets representing periods as long as 1 month to check their stability. We found that CNN was the most satisfactory approach, reaching 96% accuracy and detecting multiple vehicle classes at the same time at a low computational cost. Our findings show that seismic methods can be used for traffic monitoring and security purposes without violating the privacy of vehicle occupants, offering greater efficiency and lower costs than current methods. A similar approach may be useful for other types of transportation, such as vessels and airplanes.

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