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Wyniki wyszukiwania dla: ANTENNA DESIGN, EM-DRIVEN DESIGN, LEARNING BY EXAMPLES, SURROGATE MODELING, DEEP LEARNING
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Designing learning spaces through international and interdisciplinary collaborative design studio: The case of engineer architects and pedagogic students
PublikacjaThe study explores the dynamics and outcomes of an international interdisciplinary design studio focusing on innovative learning spaces. Conducted over two years between students of Faculty of Architecture at Gdansk Tech and pedagogic students from Kibbutzim College in Tel Aviv, this design-based study examines the contributions of unique educational program to student learning, the evolution of the design process, collaboration,...
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EM-Driven Size Reduction and Multi-Criterial Optimization of Broadband Circularly-Polarized Antennas Using Pareto Front Traversing and Design Extrapolation
PublikacjaMaintaining small size has become an important consideration in the design of contemporary antenna structures. In the case of broadband circularly polarized (CP) antennas, miniaturization is a challenging process due to the necessity of simultaneous handling of electrical and field properties (reflection, axial ratio, gain), as well as ensuring sufficient frequency range of operation, especially at the lower edge of the antenna...
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Data, Information, Knowledge, Wisdom Pyramid Concept Revisited in the Context of Deep Learning
PublikacjaIn this paper, the data, information, knowledge, and wisdom (DIKW) pyramid is revisited in the context of deep learning applied to machine learningbased audio signal processing. A discussion on the DIKW schema is carried out, resulting in a proposal that may supplement the original concept. Parallels between DIWK pertaining to audio processing are presented based on examples of the case studies performed by the author and her collaborators....
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On Computationally-Efficient Reference Design Acquisition for Reduced-Cost Constrained Modeling and Re-Design of Compact Microwave Passives
PublikacjaFull-wave electromagnetic (EM) analysis has been playing a major role in the design of microwave components for the last few decades. In particular, EM tools allow for accurate evaluation of electrical performance of miniaturized structures where strong cross-coupling effects cannot be adequately quantified using equivalent network models. However, EM-based design procedures (parametric optimization, statistical analysis) generate...
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Expedited simulation-driven design optimization of UWB antennas by means of response features
PublikacjaIn this work, a method for fast design optimization of broadband antennas is considered. The approach is based on a feature-based optimization (FBO) concept where reflection characteristics of the structure at hand are formulated in terms of suitably defined feature points. Redefinition of the design problem allows for reducing the design optimization cost, because the dependence of feature point coordinates on antenna dimensions...
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Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
PublikacjaFollowing the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides...
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Meta-Design and the Triple Learning Organization in Architectural Design Process
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Expedited Trust-Region-Based Design Closure of Antennas by Variable-Resolution EM Simulations
PublikacjaThe observed growth in the complexity of modern antenna topologies fostered a widespread employment of numerical optimization methods as the primary tools for final adjustment of the system parameters. This is mainly caused by insufficiency of traditional design closure approaches, largely based on parameter sweeping. Reliable evaluation of complex antenna structures requires full-wave electromagnetic (EM) analysis. Yet, EM-driven...
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Expedited Metaheuristic-Based Antenna Optimization Using EM Model Resolution Management
PublikacjaDesign of modern antenna systems heavily relies on numerical opti-mization methods. Their primary purpose is performance improvement by tun-ing of geometry and material parameters of the antenna under study. For relia-bility, the process has to be conducted using full-wave electromagnetic (EM) simulation models, which are associated with sizable computational expendi-tures. The problem is aggravated in the case of global optimization,...
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Rapid design closure of linear microstrip antenna array apertures using response features
PublikacjaA simple yet reliable approach to a rapid design closure of linear antenna array apertures at the electromagnetic (EM)-simulation level is proposed. Our methodology exploits an underlying array factor (AF) model suitably corrected by means of characteristic points (angles and levels) of the radiation pattern of the EM model of the antenna array aperture. This conveniently allows for controlling both the side lobe levels...
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Cost-Efficient Multi-Objective Design of Miniaturized Microwave Circuits Using Machine Learning and Artificial Neural Network
PublikacjaDesigning microwave components involves managing multiple objectives such as center frequencies, impedance matching, and size reduction for miniaturized structures. Traditional multi-objective optimization (MO) approaches heavily rely on computationally expensive population-based methods, especially when exe-cuted with full-wave electromagnetic (EM) analysis to guarantee reliability. This paper introduces a novel and cost-effective...
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Rapid Multi-Criterial Antenna Optimization by Means of Pareto Front Triangulation and Interpolative Design Predictors
PublikacjaModern antenna systems are designed to meet stringent performance requirements pertinent to both their electrical and field properties. The objectives typically stay in conflict with each other. As the simultaneous improvement of all performance parameters is rarely possible, compromise solutions have to be sought. The most comprehensive information about available design trade-offs can be obtained through multi-objective optimization...
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Expedited Variable-Resolution Surrogate Modeling of Miniaturized Microwave Passives in Confined Domains
PublikacjaDesign of miniaturized microwave components is largely based on computational models, primarily, full-wave electromagnetic (EM) simulations. EM analysis is capable of giving an accurate account for cross-coupling effects, substrate and radiation losses, or interactions with environmental components (e.g., connectors). Unfortunately, direct execution of EM-based design tasks such as parametric optimization or uncertainty quantification,...
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EM-Driven Multi-Objective Design of Impedance Transformers By Pareto Ranking Bisection Algorithm
PublikacjaIn the paper, the problem of fast multi-objective optimization of compact impedance matching transformers is addressed by utilizing a novel Pareto ranking bisection algorithm. It approximates the Pareto front by dividing line segments connecting the designs found in the previous iterations, and refining the obtained candidate solutions by means of poll-type search involving Pareto ranking. The final Pareto set is obtained using...
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Kriging metamodels and design re‐utilization for fast parameter tuning of antenna structures
PublikacjaThe paper addresses the problem of computationally efficient electromagnetic (EM)‐driven design closure of antenna structures. The foundations of the presented approach are fast kriging interpolation metamodels, utilized for two purposes: (a) producing a good starting point for further parameter tuning, and (b) yielding a reasonable Jacobian matrix estimate to jump‐start the optimization procedure. The models are rendered using...
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Strategies for computationally feasible multi-objective simulation-driven design of compact RF/microwave components
PublikacjaMulti-objective optimization is indispensable when possible trade-offs between various (and usually conflicting) design objectives are to be found. Identification of such design alternatives becomes very challenging when performance evaluation of the structure/system at hand is computationally expensive. Compact RF and microwave components are representative examples of such a situation: due to highly compressed layouts and considerable...
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Fast EM-Driven Nature-Inspired Optimization of Antenna Input Characteristics Using Response Features and Variable-Resolution Simulation Models
PublikacjaUtilization of optimization technique is a must in the design of contemporary antenna systems. Often, global search methods are necessary, which are associated with high computational costs when conducted at the level of full-wave electromagnetic (EM) models. In this study, we introduce an innovative method for globally optimizing reflection responses of multi-band antennas. Our approach uses surrogates constructed based on response...
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Cost-Efficient EM-Driven Size Reduction of Antenna Structures by Multi-Fidelity Simulation Models
PublikacjaDesign of antenna systems for emerging application areas such as the Internet of Things (IoT), fifth generation wireless communications (5G), or remote sensing, is a challenging endeavor. In addition to meeting stringent performance specifications concerning electrical and field properties, the structure has to maintain small physical dimensions. The latter normally requires searching for trade-off solutions because miniaturization...
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Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech
PublikacjaWe present a novel deep learning model for the detection and reconstruction of dysarthric speech. We train the model with a multi-task learning technique to jointly solve dysarthria detection and speech reconstruction tasks. The model key feature is a low-dimensional latent space that is meant to encode the properties of dysarthric speech. It is commonly believed that neural networks are black boxes that solve problems but do not...
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Performance‐driven modeling of compact couplers in restricted domains
PublikacjaFast surrogate models can play an important role in reducing the cost of EM-driven design closure of miniaturized microwave components. Unfortunately, construction of such models is challenging due to curse of dimensionality and wide range of geometry parameters that need to be included in order to make it practically useful. In this letter, a novel approach to design-oriented modeling of compact couplers is presented. Our method...
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Surrogate-assisted EM-driven miniaturization of wideband microwave couplers by means of co-simulation low-fidelity models
PublikacjaThis article proposes a methodology for rapid design optimization of miniaturized wideband couplers. More specifically, a class of circuits is considered, in which conventional transmission lines are replaced by their abbreviated counterparts referred to as slow-wave compact cells. Our focus is on explicit reduction of the structure size as well as on reducing the CPU cost of the design process. For the sake of computational feasibility,...
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EM-Driven Multi-Objective Optimization of a Generic Monopole Antenna by Means of a Nested Trust-Region Algorithm
PublikacjaAntenna structures for modern applications are characterized by complex and unintuitive topologies that are difficult to develop when conventional experience-driven techniques are of use. In this work, a method for automatic generation of antenna geometries in a multi-objective setup has been proposed. The approach involves optimization of a generic spline-based radiator with adjustable number of parameters using a nested trust-region-based...
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Model-free and Model-based Reinforcement Learning, the Intersection of Learning and Planning
PublikacjaMy doctoral dissertation is intended as the compound of four publications considering: structure and randomness in planning and reinforcement learning, continuous control with ensemble deep deterministic policy gradients, toddler-inspired active representation learning, and large-scale deep reinforcement learning costs.
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Low-Cost EM-Simulation-Driven Multi-Objective Optimization of Antennas
PublikacjaA surrogate-based method for efficient multi-objective antenna optimization is presented. Our technique exploits response surface approximation (RSA) model constructed from sampled low-fidelity antenna model (here, obtained through coarse-discretization EM simulation). The RSA model enables fast determination of the best available trade-offs between conflicting design goals. A low-cost RSA model construction is possible through...
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Design-oriented computationally-efficient feature-based surrogate modelling of multi-band antennas with nested kriging
PublikacjaDesign of modern antenna structures heavily depends on electromagnetic (EM) simulation tools. EM analysis provides reliable evaluation of increasingly complex designs but tends to be CPU intensive. When multiple simulations are needed (e.g., for parameters tuning), the aggregated simulation cost may become a serious bottleneck. As one possible way of mitigating the issue, the recent literature fosters utilization of faster representations,...
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Accelerated Re-Design of Antenna Structures Using Sensitivity-Based Inverse Surrogates
PublikacjaThe paper proposes a novel framework for accelerated re-design (dimension scaling) of antenna structures using inverse surrogates. The major contribution of the work is a sensitivity-based model identification procedure, which permits a significant reduction of the number of reference designs required to render the surrogate. Rigorous formulation of the approach is supplemented by its comprehensive numerical validation using a...
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Simulation-driven size-reduction-oriented design of multi-band antennas by means of response features
PublikacjaThis study addresses the problem of explicit size reduction of multi-band antennas by means of simulation-driven optimisation. The principal difficulty of electromagnetic (EM)-based miniaturisation of multi-band antennas is that several resonances have to be controlled independently (both in terms of their frequency allocation and depth) while attempting to reduce physical dimensions of the structure at hand. The design method...
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Global EM-Driven Optimization of Multi-Band Antennas Using Knowledge-Based Inverse Response-Feature Surrogates
PublikacjaElectromagnetic simulation tools have been playing an increasing role in the design of contemporary antenna structures. The employment of electromagnetic analysis ensures reliability of evaluating antenna characteristics but also incurs considerable computational expenses whenever massive simulations are involved (e.g., parametric optimization, uncertainty quantification). This high cost is the most serious bottleneck of simulation-driven...
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Deep learning techniques for biometric security: A systematic review of presentation attack detection systems
PublikacjaBiometric technology, including finger vein, fingerprint, iris, and face recognition, is widely used to enhance security in various devices. In the past decade, significant progress has been made in improving biometric sys- tems, thanks to advancements in deep convolutional neural networks (DCNN) and computer vision (CV), along with large-scale training datasets. However, these systems have become targets of various attacks, with...
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Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
PublikacjaThe field of cancer diagnostics has been revolutionized by liquid biopsies, which offer a bridge between laboratory research and clinical settings. These tests are less invasive than traditional biopsies and more convenient than routine imaging methods. Liquid biopsies allow studying of tumor-derived markers in bodily fluids, enabling the development of more precise cancer diagnostic tests for screening, disease monitoring, and...
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Rapid Surrogate-Aided Multi-Criterial Optimization of Compact Microwave Passives Employing Machine Learning and ANNs
PublikacjaThis article introduces an innovative method for achieving low-cost and reliable multi-objective optimization (MO) of microwave passive circuits. The technique capitalizes on the attributes of surrogate models, specifically artificial neural networks (ANNs), and multi-resolution electromagnetic (EM) analysis. We integrate the search process into a machine learning (ML) framework, where each iteration produces multiple infill points...
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Surrogate-Assisted Design of Checkerboard Metasurface for Broadband Radar Cross-Section Reduction
PublikacjaMetasurfaces have been extensively exploited in stealth applications to reduce radar cross section (RCS). They rely on the manipulation of backward scattering of electromagnetic (EM) waves into various oblique angles. However, arbitrary control of the scattering properties poses a significant challenge as a design task. Yet it is a principal requirement for making RCS reduction possible. This article introduces a surrogate-based...
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Fast EM-driven optimization using variable-fidelity EM models and adjoint sensitivities
PublikacjaA robust and computationally efficient technique for microwave design optimization is presented. Our approach exploits variable-fidelity electromagnetic (EM) simulation models and adjoint sensitivities. The low-fidelity EM model correction is realized by means of space mapping (SM). In the optimization process, the SM parameters are optimized together with the design itself, which allows us to keep the number...
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Training of Deep Learning Models Using Synthetic Datasets
PublikacjaIn order to solve increasingly complex problems, the complexity of Deep Neural Networks also needs to be constantly increased, and therefore training such networks requires more and more data. Unfortunately, obtaining such massive real world training data to optimize neural networks parameters is a challenging and time-consuming task. To solve this problem, we propose an easy-touse and general approach to training deep learning...
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Reduced-Cost Constrained Modeling of Microwave and Antenna Components: Recent Advances
PublikacjaElectromagnetic (EM) simulation models are ubiquitous in the design of microwave and antenna components. EM analysis is reliable but CPU intensive. In particular, multiple simulations entailed by parametric optimization or uncertainty quantification may considerably slow down the design processes. In order to address this problem, it is possible to employ fast metamodels. Here, the popular solution approaches are approximation...
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Rapid design of miniaturised branch-line couplers through concurrent cell optimisation and surrogate-assisted fine-tuning
PublikacjaIn this study, the authors introduce a methodology for low-cost simulation-driven design optimisation of highly miniaturised branch-line couplers (BLCs). The first stage of their design approach exploits fast concurrent optimisation of geometrically dependent, but electromagnetically isolated cells that constitute a BLC. The cross-coupling effects between the cells are taken into account in the second stage, where a surrogate-assisted...
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Specification-Oriented Automatic Design of Topologically Agnostic Antenna Structure
PublikacjaDesign of antennas for modern applications is a challenging task that combines cognition-driven development of topology intertwined with tuning of its parameters using rigorous numerical optimization. However, the process can be streamlined by neglecting the engineering insight in favor of automatic de-termination of structure geometry. In this work, a specification-oriented design of topologically agnostic antenna is considered....
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Deep learning based thermal image segmentation for laboratory animals tracking
PublikacjaAutomated systems for behaviour classification of laboratory animals are an attractive alternative to manual scoring. However, the proper animals separation and tracking, especially when they are in close contact, is the bottleneck of the behaviour analysis systems. In this paper, we propose a method for the segmentation of thermal images of laboratory rats that are in close contact during social behaviour tests. For this, we are...
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E-learning courses
Kursy OnlineStrona zawiera zbiór kursów prowadzonych metodą e-learning. Kursy te są skierowane do studentów I stopnia kierunku informatyka na VII semestrze profilu Bazy danych, do studentów na kierunku informatyka na II semestrze studiów II stopnia na specjalności ZAD i ISI.
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Variable Data Structures and Customized Deep Learning Surrogates for Computationally Efficient and Reliable Characterization of Buried Objects
PublikacjaIn this study, in order to characterize the buried object via deep-learning-based surrogate modeling approach, 3-D full-wave electromagnetic simulations of a GPR model has been used. The task is to predict simultaneously and independent of each characteristic parameters of a buried object of several radii at different positions (depth and lateral position) in various dispersive subsurface media. This study has analyzed variable...
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Journal of Formative Design in Learning
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Patch size setup and performance/cost trade-offs in multi-objective EM-driven antenna optimization using sequential domain patching
PublikacjaPurpose This paper aims to assess control parameter setup and its effect on computational cost and performance of deterministic procedures for multi-objective design optimization of expensive simulation models of antenna structures. Design/methodology/approach A deterministic algorithm for cost-efficient multi-objective optimization of antenna structures has been assessed. The algorithm constructs a patch connecting extreme Pareto-optimal...
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Machine learning for PhD students
Kursy OnlineAn introductory course in machine learning for PhD students from Department of Geotechnical and Hydraulic Engineering
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Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks
PublikacjaThe increasing use of the Internet with vehicles has made travel more convenient. However, hackers can attack intelligent vehicles through various technical loopholes, resulting in a range of security issues. Due to these security issues, the safety protection technology of the in-vehicle system has become a focus of research. Using the advanced autoencoder network and recurrent neural network in deep learning, we investigated...
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Rapid multi-objective simulation-driven design of compact microwave circuits
PublikacjaA methodology for rapid multi-objective design of compact microwave circuits is proposed. Our approach exploits point-by-point Pareto set identification using surrogate-based optimization techniques, auxiliary equivalent circuit models, and space mapping as the major model correction method. The proposed technique is illustrated and validated through the design of a compact rat-race coupler. A set of ten designs being trade-offs...
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Scalability of surrogate-assisted multi-objective optimization of antenna structures exploiting variable-fidelity electromagnetic simulation models
PublikacjaMulti-objective optimization of antenna structures is a challenging task due to high-computational cost of evaluating the design objectives as well as large number of adjustable parameters. Design speedup can be achieved by means of surrogate-based optimization techniques. In particular, a combination of variable-fidelity electromagnetic (EM) simulations, design space reduction techniques, response surface approximation (RSA) models,...
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Robust Parameter Tuning of Antenna Structures by Means of Design Specification Adaptation
PublikacjaParameter tuning through numerical optimization has become instrumental in the design of high-performance antenna systems. Yet, practical optimization faces several major challenges, including high cost of massive evaluations of antenna characteristics, normally involving full-wave electromagnetic (EM) analysis, large numbers of adjustable variables, the shortage of reasonable initial solutions in the case of topologically complex...
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Fast Multi-Objective Antenna Optimization Using Sequential Patching and Variable-Fidelity EM Models
PublikacjaIn this work, a technique for fast multi-objective design optimization of antenna structures is presented. In our approach, the initial approximation of the Pareto set representing the best possible trade-offs between conflicting design objectives is obtained by means of sequential patching of the design space. The latter is a stencil-based search that aims at creating a path that connects the extreme Pareto-optimal designs (obtained...
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Bimodal deep learning model for subjectively enhanced emotion classification in films
PublikacjaThis research delves into the concept of color grading in film, focusing on how color influences the emotional response of the audience. The study commenced by recalling state-of-the-art works that process audio-video signals and associated emotions by machine learning. Then, assumptions of subjective tests for refining and validating an emotion model for assigning specific emotional labels to selected film excerpts were presented....
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Fundamentals of Physics-Based Surrogate Modeling
PublikacjaChapter 1 was focused on data-driven (or approximation-based) modeling methods. The second major class of surrogates are physics-based models outlined in this chapter. Although they are not as popular, their importance is growing because of the challenges related to construction and handling of approximation surrogates for many real-world problems. The high cost of evaluating computational models, nonlinearity of system responses,...