Despite great progress in simulating multiphysics problems using the numerical discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate noisy data into existing algorithms, mesh generation remains complex, and high-dimensional problems governed by parameterized PDEs cannot be tackled. Moreover, solving inverse problems with hidden physics is often prohibitively expensive and requires different formulations and elaborate computer codes. Machine learning has emerged as a promising alternative, but training deep neural networks requires big data, not always available for scientific problems. Instead, such networks can be trained from additional information obtained by enforcing the physical laws (for example, at random points in the continuous space-time domain). Such physics-informed learning integrates (noisy) data and mathematical models, and implements them through neural networks or other kernel-based regression networks. Moreover, it may be possible to design specialized network architectures that automatically satisfy some of the physical invariants for better accuracy, faster training and improved generalization. Here, we review some of the prevailing trends in embedding physics into machine learning, present some of the current capabilities and limitations and discuss diverse applications of physics-informed learning both for forward and inverse problems, including discovering hidden physics and tackling high-dimensional problems. The rapidly developing field of physics-informed learning integrates data and mathematical models seamlessly, enabling accurate inference of realistic and high-dimensional multiphysics problems. This Review discusses the methodology and provides diverse examples and an outlook for further developments.

Physics-informed machine learning

Thermophysical Properties Research Literature Retrieval Guide Edited by Y. S. Touloukian, J. K. Gerritsen and N. Y. Moore Second edition, revised and expanded. Book 1: Pp. xxi + 819. Book 2: Pp.621. Book 3: Pp. ix + 1315. (New York: Plenum Press, 1967.) n.p.

/pdf/heat-and-mass-transfer-1yaijfx8vp.pdf

Heat and Mass Transfer

Internet of Things (IoT) is reshaping the incumbent industry to smart industry featured with data-driven decision-making. However, intrinsic features of IoT result in a number of challenges, such as decentralization, poor interoperability, privacy, and security vulnerabilities. Blockchain technology brings the opportunities in addressing the challenges of IoT. In this paper, we investigate the integration of blockchain technology with IoT. We name such synthesis of blockchain and IoT as blockchain of things (BCoT). This paper presents an in-depth survey of BCoT and discusses the insights of this new paradigm. In particular, we first briefly introduce IoT and discuss the challenges of IoT. Then, we give an overview of blockchain technology. We next concentrate on introducing the convergence of blockchain and IoT and presenting the proposal of BCoT architecture. We further discuss the issues about using blockchain for fifth generation beyond in IoT as well as industrial applications of BCoT. Finally, we outline the open research directions in this promising area.

Blockchain for Internet of Things: A Survey

Control Systems Engineering

'Blogging' - a contraction of the term 'web logging' - is perhaps best described as a form of micro-publishing. Easy to use, from any Internet connection point, blogging has become firmly established as a web based communications tool. The blogging phenomenon has evolved from its early origin as a medium for the publication of simple, online personal diaries, to the latest disruptive technology, the 'killer app' that has the capacity to engage people in collaborative activity, knowledge sharing, reflection and debate (Hiler, 2003). Many blogs have large and dedicated readerships, and blog clusters have formed linking fellow bloggers in accordance with their common interests. 
This paper explores the potential of blogs as learning spaces for students in the higher education sector. It refers to the nascent literature on the subject, explores methods for using blogs for educational purposes in university courses (eg. Harvard Law School), and records the experience of the Brisbane Graduate School of Business at Queensland University of Technology, with its 'MBA blog'. The paper concludes that blogging has the potential to be a transformational technology for teaching and learning.

Exploring the use of blogs as learning spaces in the higher education sector

A method for solving boundary value problems (BVPs) is introduced using artificial neural networks (ANNs) for irregular domain boundaries with mixed Dirichlet/Neumann boundary conditions (BCs). The approximate ANN solution automatically satisfies BCs at all stages of training, including before training commences. This method is simpler than other ANN methods for solving BVPs due to its unconstrained nature and because automatic satisfaction of Dirichlet BCs provides a good starting approximate solution for significant portions of the domain. Automatic satisfaction of BCs is accomplished by the introduction of an innovative length factor. Several examples of BVP solution are presented for both linear and nonlinear differential equations in two and three dimensions. Error norms in the approximate solution on the order of 10-4 to 10-5 are reported for all example problems.

Artificial Neural Network Method for Solution of Boundary Value Problems With Exact Satisfaction of Arbitrary Boundary Conditions

\n In the Monte Carlo ray-trace (MCRT) method, millions of rays are emitted and traced throughout an enclosure following the laws of geometrical optics. Each ray represents the path of a discrete quantum of energy emitted from surface element i and eventually absorbed by surface element j. The distribution of rays absorbed by the n surface elements making up the enclosure is interpreted in terms of a radiation distribution factor matrix whose elements represent the probability that energy emitted by element i will be absorbed by element j. Once obtained, the distribution factor matrix may be used to compute the net heat flux distribution on the walls of an enclosure corresponding to a specified surface temperature distribution. It is computationally very expensive to obtain high accuracy in the heat transfer calculation when high spatial resolution is required. This is especially true if a manifold of emissivities is to be considered in a parametric study in which each value of surface emissivity requires a new ray-trace to determine the corresponding distribution factor matrix. Artificial neural networks (ANNs) offer an alternative approach whose computational cost is greatly inferior to that of the traditional MCRT method. Significant computational efficiency is realized by eliminating the need to perform a new ray trace for each value of emissivity. The current contribution introduces and demonstrates through case studies estimation of radiation distribution factor matrices using ANNs and their subsequent use in radiation heat transfer calculations.

/pdf/artificial-neural-networks-in-radiation-heat-transfer-2yt9lqsinb.pdf

Artificial Neural Networks in Radiation Heat Transfer Analysis

The length factor artificial neural network (ANN) method for solving coupled systems of partial differential equations (DEs) is unique among ANN methods in that the approximate solution exactly satisfies boundary conditions (BCs) on arbitrary geometries regardless of the ANN output. Besides removing the BC constraint from the optimization process, this property allows the method to accurately solve problems with discontinuous BCs despite the continuous nature of ANNs. An automated design parameter selection process is developed to choose a single ANN from an ensemble comprising numerous combinations of design parameters and random starting weights and biases. The selection is made completely independently of the human designer by comparing the magnitude and uniformity of each approximate solution's error in satisfying the DE(s). The automated selection process successfully chooses a solution with error on the same order of magnitude as the best solution in the ensemble. The resulting approximations provide low error solutions for the three different thermal-fluid science example problems explored, including the Navier–Stokes equations.

Automated design parameter selection for neural networks solving coupled partial differential equations with discontinuities

\n This study presents the design analysis and development of a novel partially compliant bistable mechanism. Motion behavior dependence on links and relative angles are analyzed, lumped parameter model is derived, mechanism parts including the compliant members are three-dimensional (3D) printed and a state feedback controller is implemented so that the slider follows a well-defined trajectory if designed as an actuator. The proposed mechanism consists of initially straight, large deflecting fixed-pinned compliant links, rigid links, and a sliding mass. Dynamic response of the mechanism is studied using elliptic integral solutions, pseudo rigid body model (PRBM), vector closure loop equations and Elliptic integrals. Nonlinear model is simulated in matlab simulink using fourth‐order Runge‐Kutta algorithms. The research emphasizes on the realization and dynamic response of the mechanism and the trajectory control of the slider so that the slider can be kept constant at specified distances resulting a dwell motion if designed as a linear actuator.

Design, Analysis, Experimentation, and Control of a Partially Compliant Bistable Mechanism

Artificial Intelligence and Autonomous Vehicles

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