Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

/pdf/digital-twin-values-challenges-and-enablers-from-a-modeling-3hpoelaogn.pdf

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

This paper reviews the basic concepts of rays, ray tracing algorithms, and radio propagation modeling using ray tracing methods We focus on the fundamental concepts and the development of practical ray tracing algorithms The most recent progress and a future perspective of ray tracing are also discussed We envision propagation modeling in the near future as an intelligent, accurate, and real-time system in which ray tracing plays an important role This review is especially useful for experts who are developing new ray tracing algorithms to enhance modeling accuracy and improve computational speed

Ray Tracing for Radio Propagation Modeling: Principles and Applications

From the material presented here, it is clear that the theory underlying the SNF approach is complex and involved to implement. However, it is also very elegant and provides one with many measurement options and powerful capabilities. The numerical implementation of the theory can be efficiently deployed through the use of the fast Fourier transform (FFT) enabling transforms of even electrically large antennas to be accomplished in a matter of a few seconds on a modern powerful digital computer. With the advent of commercially available SNF test systems, the user can exploit these techniques, largely unimpeded by the burden of the theory or the implementation thereof. The material presented here highlighted some of the fundamental concepts and limitations the user needs to be aware of in order to use these test systems with confidence.

/pdf/spherical-near-field-antenna-measurements-5bm37osur0.pdf

Spherical near-field antenna measurements

The application scenarios and requirements are more diverse in the fifth-generation (5G) era than before. In order to successfully support the system design and deployment, accurate channel modeling is important. Ray-tracing (RT) based deterministic modeling approach is accurate with detailed angular information and is a suitable candidate for predicting time-varying channel and multiple-input multiple-output (MIMO) channel for various frequency bands. However, the computational complexity and the utility of RT are the main concerns of users. Aiming at 5G and beyond wireless communications, this paper presents a comprehensive tutorial on the design of RT and the applications. The role of RT and the state-of-the-art RT techniques are reviewed. The features of academic and commercial RT based simulators are summarized and compared. The requirements, challenges, and developing trends of RT to enable the visions are discussed. The practices of the design of high-performance RT simulation platform for 5G and beyond communications are introduced, with the publicly available high-performance cloud-based RT simulation platform as the main reference. The hardware structure, networking, workflow, data flow and fundamental functions of a flexible high-performance RT platform are discussed. The applications of high-performance RT are presented based on two 5G scenarios, i.e., a 3.5 GHz Beijing vehicle-to-infrastructure scenario and a 28 GHz Manhattan outdoor scenario. The questions on how to calibrate and validate RT based on measurements, how to apply RT for mobile communications in moving scenarios, and how to evaluate MIMO beamforming technologies are answered. This tutorial will be especially useful for researchers who work on RT algorithms development and channel modeling to meet the evaluation requirements of 5G and beyond technologies.

The Design and Applications of High-Performance Ray-Tracing Simulation Platform for 5G and Beyond Wireless Communications: A Tutorial

Structural reliability analysis aims at computing the probability of failure of systems whose performance may be assessed by using complex computational models (e.g., expensive-to-run finite-element models). A direct use of Monte Carlo simulation is not feasible in practice, unless a surrogate model (such as kriging, also known as Gaussian process modeling) is used. Such metamodels are often used in conjunction with adaptive experimental designs (i.e., design enrichment strategies), which allows one to iteratively increase the accuracy of the surrogate for the estimation of the failure probability while keeping low the overall number of runs of the costly original model. This paper develops a new structural reliability method based on the recently developed polynomial-chaos kriging (PC-kriging) approach coupled with an active learning algorithm known as adaptive kriging Monte Carlo simulation (AK-MCS). The problem is formulated in such a way that the computation of both small probabilities of fail...

/pdf/rare-event-estimation-using-polynomial-chaos-kriging-4h32ckg29l.pdf

Rare Event Estimation Using Polynomial-Chaos Kriging

https://hal.archives-ouvertes.fr/hal-01143146/document

A new surrogate modeling technique combining Kriging and polynomial chaos expansions - Application to uncertainty analysis in computational dosimetry

This paper presents a statistical assessment of scattered field from a building facade having rando m physical and geometrical parameters. A simple inhomogeneous model is considered for the building and the calculation met hod is based on Green's functions. The basis of polynomial chaos expansion method is explained and applied to estimate the sca ttered electric field from a building facade having 8 random parameters, in specular and non-specular scenarios. Uncertainty analysis and total output distribution are discussed in differen t diffraction zones of the building. IRELESS communications, particularly mobile networks, experienced a great expansion worldwide. These networks are very dense in urban areas and th e electromagnetic field distribution is highly depend ent on city structures. It is essential to have predictive tool s to assess as accurately as possible the distribution of electrom agnetic fields in order to enable optimized implementation of base stations and respect the constraints regarding huma n exposure. Wave propagation simulators are based on different models of urban environment with different assumptions on the building architectural details. Given the adopted model, the y incorporate appropriate calculation methods. We can mention empirical, statistical, theoretical, site-specific models or a combination of them to generate a hybrid model (1). Empirical models (2) are based on the extensive measurement campaigns and are highly related to the structure of the city . Statistical models (3) are also based on measurements and are u sed to give wide band multipath model of the environment. Theoretical (physical) (4) models are obtained by i mposing some general idealized conditions on the geometry. They usually use physical optics with or without diffrac tion phenomena. Site-specific models (5) depend on a lar ge number of parameters which are related to a given s ite and their computing time, depending on the complexity o f the environment, can be long. These models usually employ ray

https://hal.archives-ouvertes.fr/hal-01432519/document

Stochastic Analysis of Scattered Field by Building Facades Using Polynomial Chaos

Computer simulation has emerged as a key tool for designing and assessing engineering
systems in the last two decades. Uncertainty quantification has become
popular more recently as a way to model all the uncertainties affecting the system
and their impact onto its performance.
In this respect meta-models (a.k.a. surrogate models) have gained interest. Indeed
dealing with uncertainties requires running the computer model many times,
which may not be affordable for complex models. Surrogate models mimic the behaviour
of the original model while being cheap to evaluate.
Polynomial chaos expansion (PCE) and Kriging are two popular techniques, which
have been developed with very little interaction so far. In this report we present a new
approach, called PC-Kriging, that combines the two tools. The algorithm is based on
the universal Kriging model where the trend is represented by a set or orthonormal
polynomials.
Various aspects of the new metamodelling technique are presented and investigated
in details. The discussion starts with a survey on methods for generating an
optimal design of experiments (DOE). The PC-Kriging algorithm inherits many parameters
and sub-methods such as the number of polynomial terms and the choice
of the autocorrelation kernel. A variety of kernels are presented and discussed.
The methods are compared on analytical benchmark functions. The conclusion
of this report is that PC-Kriging performs better or at least as well as PCE or Kriging
taken separately in terms of relative generalized error (L2-error).

Combining Polynomial Chaos Expansions and Kriging

Since 20 years large efforts have been spent to assess numerically the exposure induced by wireless communication systems. Taking advantage of the improvement of computers, large efforts have been carried out to improve the numerical methods and models used to assess the electric field and the SAR in tissues exposed. Today the versatile use of wireless systems has brought large variability in possible exposure configurations. In spite of progress in high-performance computing the needed computational time does not allow using methods such as Monte Carlo. To overcome this difficulty the exposure can be considered as a polynomial expansion with a model based on the Polynomial Chaos (PC). In this paper, the PC is used to characterize the exposure for two configurations. The efficiency and accuracy of such a method in dosimetry is also discussed.

http://ieeexplore.ieee.org/iel7/6526699/6546197/06546636.pdf

Handle variability in numerical exposure assessment: The Challenge of the Stochastic Dosimetry

The perceived quality of cane reeds used on saxophones or clarinets may be very different from one reed to another even though the reeds have the same shape and strength. The aim of this work is to better understand the differences in the perceived quality of reeds by making use of acoustical measurements. A perceptual study, involving a panel of 10 musicians, was first conducted on a set of 20 reeds of the same strength. Each musician assessed each of the 20 reeds according to three descriptors: Brightness, Softness, and Global quality. Second, signal recordings during saxophone playing (saxophone playing by a musician in the laboratory, called in vivo measurements) were made of the pressures in a player's mouth, in the mouthpiece, and at the bell of the instrument. These measurements enable us to deduce specific acoustical variables, such as the threshold pressure or the spectral centroid of the notes. After an analysis of the perceptual and acoustical data (assessment of the agreement among the assessors and the main consensual differences between the reeds), correlations between the perceptual and acoustical data were performed. A modeling of the descriptors Brightness and Softness according to the acoustical variables is proposed using multiple linear regression. Results show that the pressure in the mouth at the beginning of the permanent regime is an important variable to predict the softness of the reed. The performance of the models in the prediction of the perceptual dimensions provides important clues for a more objective assessment of perceived reed qualities.

/pdf/investigation-of-the-relationships-between-perceived-10bd265brs.pdf

Pierric Kersaudy

Papers

A new surrogate modeling technique combining Kriging and polynomial chaos expansions - Application to uncertainty analysis in computational dosimetry

Stochastic Analysis of Scattered Field by Building Facades Using Polynomial Chaos

Combining Polynomial Chaos Expansions and Kriging

Handle variability in numerical exposure assessment: The Challenge of the Stochastic Dosimetry

Investigation of the Relationships Between Perceived Qualities and Sound Parameters of Saxophone Reeds