Exploring Elastoplastic Constitutive Law of Microstructured Materials Through Artificial Neural Network—A Mechanistic-Based Data-Driven Approach

2020 ◽  
Vol 87 (9) ◽  
Author(s):  
Hang Yang ◽  
Hai Qiu ◽  
Qian Xiang ◽  
Shan Tang ◽  
Xu Guo
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Complex Loading ◽  
Plasticity Theory ◽  
Constitutive Law ◽  
Data Driven ◽  
Reverse Loading ◽  
Data Driven Approach ◽  
Elastoplastic Materials

Abstract In this paper, a data-driven approach for constructing elastoplastic constitutive law of microstructured materials is proposed by combining the insights from plasticity theory and the tools of artificial intelligence (i.e., constructing yielding function through ANN) to reduce the required amount of data for machine learning. Illustrative examples show that the constitutive laws constructed by the present approach can be used to solve the boundary value problems (BVPs) involving elastoplastic materials with microstructures under complex loading paths (e.g., cyclic/reverse loading) effectively. The limitation of the proposed approach is also discussed.

Application of Machine Learning in Turbulent Combustion for Aviation Gas Turbine Combustor Design

2021 ◽  
Author(s):  
Vishwas Verma ◽  
Kiran Manoharan ◽  
Jaydeep Basani
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Flow Field ◽  
Gas Turbine ◽  
Computational Cost ◽  
Data Driven ◽  
Navier Stokes ◽  
Computational Time ◽  
Reacting Flow ◽  
Data Driven Approach

Abstract Numerical simulation of gas turbine combustors requires resolving a broad spectrum of length and time scales for accurate flow field and emission predictions. Reynold’s Averaged Navier Stokes (RANS) approach can generate solutions in few hours; however, it fails to produce accurate predictions for turbulent reacting flow field seen in general combustors. On the other hand, the Large Eddy Simulation (LES) approach can overcome this challenge, but it requires orders of magnitude higher computational cost. This limits designers to use the LES approach in combustor development cycles and prohibits them from using the same in numerical optimization. The current work tries to build an alternate approach using a data-driven method to generate fast and consistent results. In this work, deep learning (DL) dense neural network framework is used to improve the RANS solution accuracy using LES data as truth data. A supervised regression learning multilayer perceptron (MLP) neural network engine is developed. The machine learning (ML) engine developed in the present study can compute data with LES accuracy in 95% lesser computational time than performing LES simulations. The output of the ML engine shows good agreement with the trend of LES, which is entirely different from RANS, and to a reasonable extent, captures magnitudes of actual flow variables. However, it is recommended that the ML engine be trained using broad design space and physical laws along with a purely data-driven approach for better generalization.

scholarly journals Artificial Neural Network and Machine Learning Based Methods for Population Estimation of Rohingya Refugees: Comparing Data-Driven and Satellite Image-Driven Approaches

2019 ◽  
Vol 06 (04) ◽  
pp. 439-455 ◽  
Author(s):  
Nahian Ahmed ◽  
Nazmul Alam Diptu ◽  
M. Sakil Khan Shadhin ◽  
M. Abrar Fahim Jaki ◽  
M. Ferdous Hasan ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Satellite Image ◽  
Population Estimation ◽  
Data Driven ◽  
Gradient Boosting ◽  
Ann Model ◽  
Data Driven Approach ◽  
Artificial Neural

Manual field-based population census data collection method is slow and expensive, especially for refugee management situations where more frequent censuses are necessary. This study aims to explore the approaches of population estimation of Rohingya migrants using remote sensing and machine learning. Two different approaches of population estimation viz., (i) data-driven approach and (ii) satellite image-driven approach have been explored. A total of 11 machine learning models including Artificial Neural Network (ANN) are applied for both approaches. It is found that, in situations where the surface population distribution is unknown, a smaller satellite image grid cell length is required. For data-driven approach, ANN model is placed fourth, Linear Regression model performed the worst and Gradient Boosting model performed the best. For satellite image-driven approach, ANN model performed the best while Ada Boost model has the worst performance. Gradient Boosting model can be considered as a suitable model to be applied for both the approaches.

scholarly journals Machine-learning computation of distance modulus for local galaxies

2020 ◽  
Vol 635 ◽  
pp. A124
Author(s):  
A. A. Elyiv ◽  
O. V. Melnyk ◽  
I. B. Vavilova ◽  
D. V. Dobrycheva ◽  
V. E. Karachentseva
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Angular Size ◽  
Data Driven ◽  
Gradient Boosting ◽  
Distance Modulus ◽  
Explanatory Variables ◽  
Photometric Redshift ◽  
The Neural Network ◽  
Data Driven Approach

Context. Quickly growing computing facilities and an increasing number of extragalactic observations encourage the application of data-driven approaches to uncover hidden relations from astronomical data. In this work we raise the problem of distance reconstruction for a large number of galaxies from available extensive observations. Aims. We propose a new data-driven approach for computing distance moduli for local galaxies based on the machine-learning regression as an alternative to physically oriented methods. We use key observable parameters for a large number of galaxies as input explanatory variables for training: magnitudes in U, B, I, and K bands, corresponding colour indices, surface brightness, angular size, radial velocity, and coordinates. Methods. We performed detailed tests of the five machine-learning regression techniques for inference of m−M: linear, polynomial, k-nearest neighbours, gradient boosting, and artificial neural network regression. As a test set we selected 91 760 galaxies at z <  0.2 from the NASA/IPAC extragalactic database with distance moduli measured by different independent redshift methods. Results. We find that the most effective and precise is the neural network regression model with two hidden layers. The obtained root–mean–square error of 0.35 mag, which corresponds to a relative error of 16%, does not depend on the distance to galaxy and is comparable with methods based on the Tully–Fisher and Fundamental Plane relations. The proposed model shows a 0.44 mag (20%) error in the case of spectroscopic redshift absence and is complementary to existing photometric redshift methodologies. Our approach has great potential for obtaining distance moduli for around 250 000 galaxies at z <  0.2 for which the above-mentioned parameters are already observed.

Authorship in the Age of Machine Learning and Artificial Intelligence

2021 ◽  
Author(s):  
J.-M. Deltorn ◽  
Franck Macrez
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Musical Work ◽  
Musical Works ◽  
New Generation ◽  
Specific Contribution

A new generation of machine learning (ML) and artificial intelligence (AI) creative tools are now at the disposal of musicians, professionals and amateurs alike. These new technical intermediaries allow the production of unprecedented forms of compositions, from generating new works by mimicking a style or by mixing a curated ensemble of musical works to letting an algorithm complete one’s own creation in unexpected directions or by letting an artist interact with the parameters of a neural network to explore fresh musical avenues. Unsurprisingly, this new spectrum of algorithmic compositions question both the nature and the degree of involvement of the creator in the musical work. As a consequence, the issue of authorship and, in particular, the assessment of the specific contribution of a (human) creator through the algorithmic pipeline may require special scrutiny when AI and ML tools are used to produce musical works.

scholarly journals Before and beyond trust: reliance in medical AI

2021 ◽  
pp. medethics-2020-107095
Author(s):  
Charalampia (Xaroula) Kerasidou ◽  
Angeliki Kerasidou ◽  
Monika Buscher ◽  
Stephen Wilkinson
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
New Technology ◽  
Public Trust ◽  
Learning Technologies ◽  
Data Driven ◽  
Policy Makers ◽  
The Public ◽  
Regulatory Frameworks ◽  
Practice Of Medicine

Artificial intelligence (AI) is changing healthcare and the practice of medicine as data-driven science and machine-learning technologies, in particular, are contributing to a variety of medical and clinical tasks. Such advancements have also raised many questions, especially about public trust. As a response to these concerns there has been a concentrated effort from public bodies, policy-makers and technology companies leading the way in AI to address what is identified as a "public trust deficit". This paper argues that a focus on trust as the basis upon which a relationship between this new technology and the public is built is, at best, ineffective, at worst, inappropriate or even dangerous, as it diverts attention from what is actually needed to actively warrant trust. Instead of agonising about how to facilitate trust, a type of relationship which can leave those trusting vulnerable and exposed, we argue that efforts should be focused on the difficult and dynamic process of ensuring reliance underwritten by strong legal and regulatory frameworks. From there, trust could emerge but not merely as a means to an end. Instead, as something to work in practice towards; that is, the deserved result of an ongoing ethical relationship where there is the appropriate, enforceable and reliable regulatory infrastructure in place for problems, challenges and power asymmetries to be continuously accounted for and appropriately redressed.

scholarly journals Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network

2020 ◽  
Vol 27 (3) ◽  
pp. 373-389 ◽  
Author(s):  
Ashesh Chattopadhyay ◽  
Pedram Hassanzadeh ◽  
Devika Subramanian
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Short Term Memory ◽  
Data Driven ◽  
Reservoir Computing ◽  
Short Term ◽  
Term Memory ◽  
Machine Learning Methods ◽  
Long Short Term Memory ◽  
Lorenz 96

Abstract. In this paper, the performance of three machine-learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multiscale spatiotemporal Lorenz 96 system is examined. The methods are an echo state network (ESN, which is a type of reservoir computing; hereafter RC–ESN), a deep feed-forward artificial neural network (ANN), and a recurrent neural network (RNN) with long short-term memory (LSTM; hereafter RNN–LSTM). This Lorenz 96 system has three tiers of nonlinearly interacting variables representing slow/large-scale (X), intermediate (Y), and fast/small-scale (Z) processes. For training or testing, only X is available; Y and Z are never known or used. We show that RC–ESN substantially outperforms ANN and RNN–LSTM for short-term predictions, e.g., accurately forecasting the chaotic trajectories for hundreds of numerical solver's time steps equivalent to several Lyapunov timescales. The RNN–LSTM outperforms ANN, and both methods show some prediction skills too. Furthermore, even after losing the trajectory, data predicted by RC–ESN and RNN–LSTM have probability density functions (pdf's) that closely match the true pdf – even at the tails. The pdf of the data predicted using ANN, however, deviates from the true pdf. Implications, caveats, and applications to data-driven and data-assisted surrogate modeling of complex nonlinear dynamical systems, such as weather and climate, are discussed.

scholarly journals Reports of the 2013 AAAI Spring Symposium Series

AI Magazine ◽  
2013 ◽  
Vol 34 (3) ◽  
pp. 93-98 ◽  
Author(s):  
Vita Markman ◽  
Georgi Stojanov ◽  
Bipin Indurkhya ◽  
Takashi Kido ◽  
Keiki Takadama ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cognitive Development ◽  
Behavior Change ◽  
Autonomous Systems ◽  
Data Driven ◽  
Intelligent Robots ◽  
Symposium Series ◽  
Development Data ◽  
Weakly Supervised

The Association for the Advancement of Artificial Intelligence was pleased to present the AAAI 2013 Spring Symposium Series, held Monday through Wednesday, March 25-27, 2013. The titles of the eight symposia were Analyzing Microtext, Creativity and (Early) Cognitive Development, Data Driven Wellness: From Self-Tracking to Behavior Change, Designing Intelligent Robots: Reintegrating AI II, Lifelong Machine Learning, Shikakeology: Designing Triggers for Behavior Change, Trust and Autonomous Systems, and Weakly Supervised Learning from Multimedia. This report contains summaries of the symposia, written, in most cases, by the cochairs of the symposium.

RESEARCHING METHODS FOR PROCESSING TEXT INFORMATION AND REVIEWING THE STAGES OF AN ARTIFICIAL INTELLIGENCE MODEL CREATION AT PRODUCING CHATBOTS

2021 ◽  
Vol 2021 (2) ◽  
pp. 19-23
Author(s):  
Anastasiya Ivanova ◽  
Aleksandr Kuz'menko ◽  
Rodion Filippov ◽  
Lyudmila Filippova ◽  
Anna Sazonova ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Text Processing ◽  
Text Format ◽  
Machine Learning Methods ◽  
Methods And Techniques ◽  
Text Information ◽  
Processing Techniques ◽  
Machine Processing

The task of producing a chatbot based on a neural network supposes machine processing of the text, which in turn involves using various methods and techniques for analyzing phrases and sentences. The article considers the most popular solutions and models for data analysis in the text format: methods of lemmatization, vectorization, as well as machine learning methods. Particular attention is paid to the text processing techniques, after their analyzing the best method was identified and tested.

A note on no-free-lunch theorem

2021 ◽  
Author(s):  
Lidong Wu
Keyword(s):  
Machine Learning ◽  
Theoretical Result ◽  
Data Driven ◽  
Np Hard ◽  
Free Lunch ◽  
No Free Lunch Theorem ◽  
Data Driven Approach ◽  
Hard Problems ◽  
No Free Lunch ◽  
Np Hard Problems

The No-Free-Lunch theorem is an interesting and important theoretical result in machine learning. Based on philosophy of No-Free-Lunch theorem, we discuss extensively on the limitation of a data-driven approach in solving NP-hard problems.

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