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Next
 Fritzson, Peter A., 1952
 Hoboken, N.J. : Wiley : IEEE Press, c2011.
 Description
 Book — 1 online resource (xiii, 211 p.) : ill.
 Summary

 Preface xi
 1. Basic Concepts 1 1.1 Systems and Experiments, 2 1.1.1 Natural and Artificial Systems, 3 1.1.2 Experiments, 5 1.2 The Model Concept, 6 1.3 Simulation, 7 1.3.1 Reasons for Simulation, 8 1.3.2 Dangers of Simulation, 9 1.4 Building Models, 10 1.5 Analyzing Models, 12 1.5.1 Sensitivity Analysis, 12 1.5.2 ModelBased Diagnosis, 13 1.5.3 Model Verification and Validation, 13 1.6 Kinds of Mathematical Models, 14 1.6.1 Kinds of Equations, 15 1.6.2 Dynamic Versus Static Models, 16 1.6.3 ContinuousTime Versus DiscreteTime Dynamic Models, 17 1.6.4 Quantitative Versus Qualitative Models, 18 1.7 Using Modeling and Simulation in Product Design, 19 1.8 Examples of System Models, 21 1.9 Summary, 27 1.10 Literature, 27
 2. A Quick Tour of Modelica 29 2.1 Getting Started with Modelica, 30 2.1.1 Variables and Predefined Types, 35 2.1.2 Comments, 37 2.1.3 Constants, 38 2.1.4 Variability, 38 2.1.5 Default start Values, 39 2.2 ObjectOriented Mathematical Modeling, 39 2.3 Classes and Instances, 41 2.3.1 Creating Instances, 42 2.3.2 Initialization, 43 2.3.3 Specialized Classes, 44 2.3.4 Reuse of Classes by Modifications, 45 2.3.5 Builtin Classes and Attributes, 46 2.4 Inheritance, 47 2.5 Generic Classes, 48 2.5.1 Class Parameters as Instances, 48 2.5.2 Class Parameters as Types, 50 2.6 Equations, 51 2.6.1 Repetitive Equation Structures, 53 2.6.2 Partial Differential Equations, 54 2.7 Acausal Physical Modeling, 54 2.7.1 Physical Modeling Versus BlockOriented Modeling, 55 2.8 The Modelica Software Component Model, 57 2.8.1 Components, 58 2.8.2 Connection Diagrams, 58 2.8.3 Connectors and Connector Classes, 60 2.8.4 Connections, 61 2.8.5 Implicit Connections with Inner/Outer, 62 2.8.6 Expandable Connectors for Information Buses, 63 2.8.7 Stream Connectors, 64 2.9 Partial Classes, 65 2.9.1 Reuse of Partial Classes, 66 2.10 Component Library Design and Use, 67 2.11 Example: Electrical Component Library, 67 2.11.1 Resistor, 68 2.11.2 Capacitor, 68 2.11.3 Inductor, 68 2.11.4 Voltage Source, 69 2.11.5 Ground, 70 2.12 Simple Circuit Model, 70 2.13 Arrays, 72 2.14 Algorithmic Constructs, 74 2.14.1 Algorithm Sections and Assignment Statements, 75 2.14.2 Statements, 76 2.14.3 Functions, 77 2.14.4 Operator Overloading and Complex Numbers, 79 2.14.5 External Functions, 81 2.14.6 Algorithms Viewed as Functions, 82 2.15 Discrete Event and Hybrid Modeling, 83 2.16 Packages, 87 2.17 Annotations, 89 2.18 Naming Conventions, 91 2.19 Modelica Standard Libraries, 91 2.20 Implementation and Execution of Modelica, 94 2.20.1 Hand Translation of the Simple Circuit Model, 96 2.20.2 Transformation to State Space Form, 98 2.20.3 Solution Method, 99 2.21 History, 103 2.22 Summary, 107 2.23 Literature, 108 2.24 Exercises, 110
 3. Classes and Inheritance 113 3.1 Contract Between Class Designer and User, 113 3.2 A Class Example, 114 3.3 Variables, 115 3.3.1 Duplicate Variable Names, 116 3.3.2 Identical Variable Names and Type Names, 116 3.3.3 Initialization of Variables, 117 3.4 Behavior as Equations, 117 3.5 Access Control, 119 3.6 Simulating the Moon Landing Example, 120 3.7 Inheritance, 123 3.7.1 Inheritance of Equations, 124 3.7.2 Multiple Inheritance, 124 3.7.3 Processing Declaration Elements and Use Before Declare, 126 3.7.4 Declaration Order of extends Clauses, 127 3.7.5 The MoonLanding Example Using Inheritance, 128 3.8 Summary, 130 3.9 Literature, 130
 4. System Modeling Methodology 131 4.1 Building System Models, 131 4.1.1 Deductive Modeling Versus Inductive Modeling, 132 4.1.2 Traditional Approach, 133 4.1.3 ObjectOriented ComponentBased Approach, 134 4.1.4 TopDown Versus BottomUp Modeling, 136 4.1.5 Simplification of Models, 136 4.2 Modeling a Tank System, 138 4.2.1 Using the Traditional Approach, 138 4.2.2 Using the ObjectOriented ComponentBased Approach, 139 4.2.3 Tank System with a Continuous PI Controller, 141 4.2.4 Tank with Continuous PID Controller, 144 4.2.5 Two Tanks Connected Together, 147 4.3 TopDown Modeling of a DC Motor from Predefined Components, 148 4.3.1 Defining the System, 149 4.3.2 Decomposing into Subsystems and Sketching Communication, 149 4.3.3 Modeling the Subsystems, 150 4.3.4 Modeling Parts in the Subsystems, 151 4.3.5 Defining the Interfaces and Connections, 153 4.4 Designing InterfacesConnector Classes, 153 4.5 Summary, 155 4.6 Literature, 155
 5. The Modelica Standard Library 157 5.1 Summary, 168 5.2 Literature, 168 A. Glossary 169 Literature, 174 B. OpenModelica and OMNotebook Commands 175 B.1 OMNotebook Interactive Electronic Book, 175 B.2 Common Commands and Small Examples, 178 B.3 Complete List of Commands, 179 B.4 OMShell and Dymola, 185 OMShell, 185 Dymola Scripting, 185 Literature, 186 C. Textual Modeling with OMNotebook and DrModelica 187 C.1 HelloWorld, 188 C.2 Try DrModelica with VanDerPol and DAEExample Models, 189 C.3 Simple Equation System, 189 C.4 Hybrid Modeling with BouncingBall, 189 C.5 Hybrid Modeling with Sample, 190 C.6 Functions and Algorithm Sections, 190 C.7 Adding a Connected Component to an Existing Circuit, 190 C.8 Detailed Modeling of an Electric Circuit, 191 C.8.1 Equations, 191 C.8.2 Implementation, 192 C.8.3 Putting the Circuit Together, 195 C.8.4 Simulation of the Circuit, 195 D. Graphical Modeling Exercises 197 D.1 Simple DC Motor, 197 D.2 DC Motor with Spring and Inertia, 198 D.3 DC Motor with Controller, 198 D.4 DC Motor as a Generator, 199 References 201 Index 207.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Couretas, Jerry M., 1966 author.
 Hoboken, NJ : Wiley, 2019.
 Description
 Book — 1 online resource.
 Summary

 1 Brief Review of Cyber Incidents 1 1.1 Cyber's Emergence as an Issue 3 1.2 Estonia and Georgia  Militarization of Cyber 4 1.3 Conclusions 6
 2 Cyber Security  An Introduction to Assessment and Maturity Frameworks 9 2.1 Assessment Frameworks 9 2.2 NIST 800 Risk Framework 9 2.2.1 Maturity Models 12 2.2.2 Use Cases/Scenarios 13 2.3 Cyber Insurance Approaches 14 2.3.1 An Introduction to Loss Estimate and Rate Evaluation for Cyber 17 2.4 Conclusions 17 2.5 Future Work 18 2.6 Questions 18
 3 Introduction to Cyber Modeling and Simulation (M&S) 19 3.1 One Approach to the Science of Cyber Security 19 3.2 Cyber Mission System Development Framework 21 3.3 Cyber Risk BowTie: Likelihood to Consequence Model 21 3.4 Semantic Network Model of Cyberattack 22 3.5 Taxonomy of Cyber M&S 24 3.6 Cyber Security as a Linear System  Model Example 25 3.7 Conclusions 26 3.8 Questions 27
 4 Technical and Operational Scenarios 29 4.1 Scenario Development 30 4.1.1 Technical Scenarios and Critical Security Controls (CSCs) 31 4.1.2 ARMOUR Operational Scenarios (Canada) 32 4.2 Cyber System Description for M&S 34 4.2.1 State Diagram Models/Scenarios of Cyberattacks 34 4.2.2 McCumber Model 35 4.2.3 Military Activity and Cyber Effects (MACE) Taxonomy 36 4.2.4 Cyber Operational Architecture Training System (COATS) Scenarios 37 4.3 Modeling and Simulation Hierarchy  Strategic Decision Making and Procurement Risk Evaluation 39 4.4 Conclusions 42 4.5 Questions 43
 5 Cyber Standards for Modeling and Simulation 45 5.1 Cyber Modeling and Simulation Standards Background 46 5.2 An Introduction to Cyber Standards for Modeling and Simulation 47 5.2.1 MITRE's (MITRE) Cyber Threat Information Standards 47 5.2.2 Cyber Operational Architecture Training System 49 5.2.3 Levels of Conceptual Interoperability 50 5.3 Standards Overview  Cyber vs. Simulation 51 5.3.1 Simulation Interoperability Standards Organization (SISO) Standards 52 5.3.2 Cyber Standards 54 5.4 Conclusions 56 5.5 Questions 57
 6 Cyber Course of Action (COA) Strategies 59 6.1 Cyber Course of Action (COA) Background 59 6.1.1 EffectsBased CyberCOA Optimization Technology and Experiments (EBCOTE) Project 59 6.1.2 Crown Jewels Analysis 60 6.1.3 Cyber Mission Impact Assessment (CMIA) Tool 61 6.1.4 Analyzing Mission Impacts of Cyber Actions 63 6.2 Cyber Defense Measurables  Decision Support System (DSS) Evaluation Criteria 64 6.2.1 Visual Analytics 65 6.2.2 Managing Cyber Events 67 6.2.3 DSS COA and VV&A 68 6.3 Cyber Situational Awareness (SA) 68 6.3.1 Active and Passive Situational Awareness for Cyber 69 6.3.2 Cyber System Monitoring and Example Approaches 69 6.4 Cyber COAs and Decision Types 70 6.5 Conclusions 71 6.6 Further Considerations 72 6.7 Questions 72
 7 Cyber ComputerAssisted Exercise (CAX) and Situational Awareness (SA) via Cyber M&S 75 7.1 Training Type and Current Cyber Capabilities 77 7.2 Situational Awareness (SA) Background and Measures 78 7.3 Operational Cyber Domain and Training Considerations 79 7.4 Cyber Combined Arms Exercise (CAX) Environment Architecture 81 7.4.1 CAX Environment Architecture with Cyber Layer 82 7.4.2 Cyber Injections into Traditional CAX  Leveraging Constructive Simulation 84 7.4.3 Cyber CAX  Individual and Group Training 85 7.5 Conclusions 86 7.6 Future Work 87 7.7 Questions 87
 8 Cyber ModelBased Evaluation Background 89 8.1 Emulators, Simulators, and Verification/Validation for Cyber System Description 89 8.2 Modeling Background 90 8.2.1 Cyber Simulators 91 8.2.2 Cyber Emulators 93 8.2.3 Emulator/Simulator Combinations for Cyber Systems 94 8.2.4 Verification, Validation, and Accreditation (VV&A) 96 8.3 Conclusions 99 8.4 Questions 100
 9 Cyber Modeling and Simulation and System Risk Analysis 101 9.1 Background on Cyber System Risk Analysis 101 9.2 Introduction to using Modeling and Simulation for System Risk Analysis with Cyber Effects 104 9.3 General Business Enterprise Description Model 105 9.3.1 Translate Data to Knowledge 107 9.3.2 Understand the Enterprise 114 9.3.3 Sampling and Cyber Attack Rate Estimation 114 9.3.4 Finding Unknown Knowns  Success in Finding Improvised Explosive Device Example 116 9.4 Cyber Exploit Estimation 116 9.4.1 Enterprise Failure Estimation due to Cyber Effects 118 9.5 Countermeasures and Work Package Construction 120 9.6 Conclusions and Future Work 122 9.7 Questions 124
 10 Cyber Modeling & Simulation (M&S) for Test and Evaluation (T&E) 125 10.1 Background 125 10.2 Cyber Range Interoperability Standards (CRIS) 126 10.3 Cyber Range Event Process and Logical Range 127 10.4 Live, Virtual, and Constructive (LVC) for Cyber 130 10.4.1 Role of LVC in Capability Development 132 10.4.2 Use of LVC Simulations in Cyber Range Events 133 10.5 Applying the Logical Range Construct to System under Test (SUT) Interaction 134 10.6 Conclusions 135 10.7 Questions 136
 11 Developing ModelBased Cyber Modeling and Simulation Frameworks 137 11.1 Background 137 11.2 Model Based Systems Engineering (MBSE) and System of Systems Description (Data Centric) 137 11.3 Knowledge Based Systems Engineering (KBSE) for Cyber Simulation 138 11.3.1 DHS and SysML Modeling for Buildings (CEPHEID VARIABLE) 139 11.3.2 The Cyber Security Modeling Language (CySeMoL) 140 11.3.3 Cyber Attack Modeling and Impact Assessment Component (CAMIAC) 140 11.4 Architecture Based Cyber System Optimization Framework 141 11.5 Conclusions 141 11.6 Questions 142
 12 Appendix: Cyber M&S Supporting Data, Tools, and Techniques 143 12.1 Cyber Modeling Considerations 143 12.1.1 Factors to Consider for Cyber Modeling 143 12.1.2 Lessons Learned from Physical Security 144 12.1.3 Cyber Threat Data Providers 146 12.1.4 Critical Security Controls (CSCs) 147 12.1.5 Situational Awareness Measures 147 12.2 Cyber Training Systems 148 12.2.1 Scalable Network Defense Trainer (NDT) 153 12.2.2 SELEX ES NetComm Simulation Environment (NCSE) 153 12.2.3 Example Cyber Tool Companies 154 12.3 Cyber Related Patents and Applications 154 12.4 Conclusions 160 Bibliography 161 Index 175.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Korn, Granino A. (Granino Arthur), 19222013
 Second edition.  Hoboken, New Jersey : John Wiley & Sons Inc., [2012]
 Description
 Book — 1 online resource.
 Summary

Now in a fully revised second edition, this work introduces dynamicsystem simulation with a main emphasis on OPEN DESIRE and DESIRE software. Offering a complete update of all material, the new edition boasts two completely new chapters on fast simulation of neural networks as well as three appendices on radialbasisfunction, fuzzybasisfunction networks, and CLEARN algorithm. A companion CD contains complete binary OPEN DESIRE modeling/simulation program packages for personalcomputer LINUX and MS Windows, DESIRE examples, source code, and a comprehensive, indexed reference manual.
(source: Nielsen Book Data)
 Han, Zhiyu, author.
 Warrendale : SAE International, 2021.
 Description
 Book — 1 online resource (372 pages)
 Summary

 Preface
 Abbreviations
 Nomenclature
 Superscript
 Subscript
 1. Introduction
 2. Combustion basis of internal combustion engines
 3. Mathematical description of reactive flow with sprays
 4. Incylinder turbulence
 5. Fuel sprays
 6. Combustion and pollutant emissions
 7. Optimization of directinjection gasoline engines
 8. Optimization of diesel and alternative fuel engines
 Index
 About the author.
 4.2.1 RANS Methodology
 4.2.2 The Classical kε Model
 4.3 RNG kε Models
 4.3.1 RNG Methodology
 4.3.2 The RNG kε Model for VariableDensity Flows
 4.3.3 Other RNG kε Model Variants
 4.4 LargeEddy Simulation
 4.4.1 LES Methodology and SubGrid Models
 4.4.1.1 Smagorinsky Model
 4.4.1.2 Dynamic Smagorinsky Model
 4.4.1.3 kEquation Model
 4.4.1.4 Dynamic Structure Model
 4.4.2 Engine Simulation Examples
 4.4.2.1 Intake and InCylinder Flows
 4.4.2.2. CycletoCycle Combustion Variation
 4.4.2.3 LowTemperature Spray Combustion
(source: Nielsen Book Data)
 Chopp, D. L. (David L.), author.
 Philadelphia, Pennsylvania : Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104), [2019]
 Description
 Book — 1 PDF (xiii, 455 pages)
 Summary

 Elementary C programming
 Parallel computing using OpenMP
 Distributed programming and MPI
 GPU programming and CUDA
 GPU programming and OpenCL
 Applications
 Ferrara, Antonella, author.
 Philadelphia, Pennsylvania : Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104), [2019]
 Description
 Book — 1 PDF (xx, 289 pages)
 Summary

 Theory of classical sliding mode control
 Introduction to higher order sliding mode control
 Constrained SMC
 Optimization SMC
 SMC for networked systems
 Adaptive SMC algorithms
 Advanced SMC of robots
 Advanced SMC of microgrids
(source: Nielsen Book Data)
 Målqvist, Axel, author.
 Philadelphia, Pennsylvania : Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104), [2021]
 Description
 Book — 1 PDF (xii, 108 pages) : illustrations
 Summary

 Multiscale problems and numerical homogenization
 Numerical analyst's review of elliptic homogenization
 Decomposition of scales in elliptic problems
 Localization of numerical correctors
 Localized orthogonal decomposition method
 Effective coefficients and periodic homogenization
 Implementation aspects
 Eigenvalue problems
 Parabolic problems
 Further applications and generalizations
(source: Nielsen Book Data)
 Antoulas, Athanasios C. (Athanasios Constantinos), 1950 author.
 Philadelphia, Pennsylvania : Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104), [2020]
 Description
 Book — 1 PDF (xii, 232 pages) : illustrations (some color)
 Summary

 The model reduction enterprise
 Systems theory sundries
 Interpolatory model reduction
 Datadriven model reduction and the Loewner modeling framework
 Optimal H2 approximation via interpolation
 Interpolatory model reduction of parameterdependent systems
 Interpolatory model reduction of nonlinear systems
 Model reduction in related norms
 Interpolatory reduction of differential algebraic systems
 Iterative solves in interpolatory projections
(source: Nielsen Book Data)
9. Methods in computational science [2021]
 Hoffman, Johan, author.
 Philadelphia, Pennsylvania : Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104), [2021]
 Description
 Book — 1 PDF (xvi, 396 pages)
 Summary

 I. Mathematics foundation. 1. Vector spaces
 1.1. Introduction
 1.2. Vector spaces
 1.3. Functions on vector spaces
 1.4. Geometric properties of vector spaces
 1.5. The p norms
 1.6. Subspace and basis
 1.7. Orthogonality
 1.8. Outlook : Banach and Hilbert spaces
 1.9. Notes
 2. Linear transformations
 2.1. Introduction
 2.2. Four fundamental vector spaces
 2.3. Vector spaces of matrices
 2.4. Square matrices as linear operators
 2.5. Matrix norms
 2.6. Orthogonal matrices
 2.7. Projectors
 2.8. Outlook : bivectors and tensors
 2.9. Notes
 II. Computer science foundation. 3. Algorithms and data structures
 3.1. Introduction
 3.2. Correctness and complexity of algorithms
 3.3. Accuracy and stability of floating point arithmetic
 3.4. Data and memory
 3.5. Data structures
 3.6. Graph algorithms
 3.7. Computer graphics and the convolution algorithm
 3.8. Outlook : simulation and time stepping
 3.9. Notes
 4. High performance computing
 4.1. Introduction
 4.2. Parallel computing
 4.3. Parallel programming models
 4.4. Accelerators
 4.5. Data centric computing
 4.6. Parallel performance
 4.7. Emerging computing platforms
 4.8. Outlook : quantum computing
 4.9. Notes
 III. Matrix factorization. 5. Direct methods for systems of linear equations
 5.1. Introduction
 5.2. Systems of linear equations
 5.3. GramSchmidt QR factorization
 5.4. Householder QR factorization
 5.5. LU factorization
 5.6. Cholesky factorization
 5.7. Block matrix algorithms
 5.8. Sparse matrix algorithms
 5.9. Outlook : differential and integral operators
 5.10. Notes
 6. Eigenvalue and singular value decompositions
 6.1. Introduction
 6.2. Complex vector spaces
 6.3. Eigenvalues and eigenvectors
 6.4. Similarity transformations and spectral theorems
 6.5. Generalized eigenvalues
 6.6. Singular value decomposition
 6.7. The QR algorithm
 6.8. The implicit QR algorithm
 6.9. Outlook : continuum mechanics
 6.10. Notes
 IV. Iterative methods. 7. Iterative methods for linear equations
 7.1. Introduction
 7.2. Convergence of iterative methods
 7.3. Error estimation
 7.4. Conditioning and stability
 7.5. Fixed point iteration
 7.6. Richardson iteration and preconditioning
 7.7. Iterative methods based on matrix splitting
 7.8. GMRES and Arnoldi iteration
 7.9. The conjugate gradient method
 7.10. Low rank matrix approximations
 7.11. Outlook : linear dynamical systems
 7.12. Notes
 8. Iterative methods for nonlinear equations
 8.1. Introduction
 8.2. Continuous and differentiable functions
 8.3. Nonlinear scalar equations
 8.4. Systems of nonlinear equations
 8.5. Recurrence relations, fractals, and chaos
 8.6. Outlook : nonlinear dynamical systems
 8.7. Notes
 V. Approximation. 9. Function approximation
 9.1. Introduction
 9.2. Optimal polynomial approximation
 9.3. Polynomial interpolation
 9.4. Regression
 9.5. Projection methods
 9.6. Transforms
 9.7. Outlook : finite element methods
 9.8. Notes
 10. Function approximation for multidimensional domains
 10.1. Introduction
 10.2. Approximation for multidimensional domains
 10.3. Structured grids
 10.4. Unstructured meshes
 10.5. Mesh refinement and coarsening
 10.6. Polynomial approximation on simplicial meshes
 10.7. The reference element
 10.8. Barycentric coordinates
 10.9. Domain decomposition methods
 10.10. Multigrid methods
 10.11. Outlook : spline approximation
 10.12. Notes
 VI. Integration. 11. Integration methods
 11.1. Introduction
 11.2. NewtonCotes quadrature
 11.3. Gauss quadrature
 11.4. The fundamental theorem of calculus
 11.5. Measures and the Lebesgue integral
 11.6. Lp spaces
 11.7. The divergence theorem
 11.8. Outlook : integral operators and kernels
 11.9. Notes
 12. Stochastic methods
 12.1. Introduction
 12.2. A very brief review of probability theory
 12.3. Stochastic processes
 12.4. Random samples
 12.5. Monte Carlo integration
 12.6. Emergence and agentbased modeling
 12.7. Outlook : model order reduction
 12.8. Notes
 VII. Differential equations. 13. Scalar initial value problems
 13.1. Introduction
 13.2. The scalar initial value problem
 13.3. Stability of the initial value problem
 13.4. Stability of time stepping methods
 13.5. A priori error analysis
 13.6. Adjoint based a posteriori error analysis
 13.7. Outlook : stochastic differential equations
 13.8. Notes
 14. Systems of initial value problems
 14.1. Introduction
 14.2. Systems of initial value problems
 14.3. Harmonic oscillators
 14.4. Energy analysis of harmonic oscillators
 14.5. Particle models
 14.6. Compartment models
 14.7. Lumped parameter models and bond graphs
 14.8. Adaptive time stepping algorithms
 14.9. Parallel time stepping algorithms
 14.10. Outlook : partial differential equations
 14.11. Notes
 VIII. Optimization and learning. 15. Optimization
 15.1. Introduction
 15.2. Convex minimization
 15.3. Gradient descent minimization
 15.4. Multiobjective and global minimization
 15.5. Constrained minimization
 15.6. Lagrange multipliers
 15.7. Design optimization
 15.8. Outlook : optimal control
 15.9. Notes
 16. Learning from data
 16.1. Introduction
 16.2. Geometric methods
 16.3. Statistical decision theory
 16.4. Deep learning
 16.5. Backpropagation
 16.6. Generative adversarial networks
 16.7. Graph neural networks
 16.8. Outlook : datadriven dynamical systems
 16.9. Notes
 IX. Epilogue.
 17. Closing remarks
(source: Nielsen Book Data)
 Philadelphia, Pennsylvania : Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104), [2022]
 Description
 Book — 1 PDF (xxxviii, 462 pages)
 Summary

 Overview and motivation / Martin W. Hess, Marco Tezzele, Gianluigi Rozza
 Finite elementbased reduced basis method in computational fluid dynamics / Federico Pichi, Maria Strazzullo, Francesco Ballarin, Gianluigi Rozza
 Certified Smagorinsky reduced basis turbulence model / Enrique Delgado Ávila, Francesco Ballarin, Gianluigi Rozza
 Finite elementbased reduced basis method for optimal flow control / Maria Strazzullo, Francesco Ballarin, Gianluigi Rozza
 Reduced basis approaches to bifurcating nonlinear parametrized partial differential equations / Federico Pichi, Francesco Ballarin, Gianluigi Rozza
 Reduced basis stabilization for convectiondominated problems / Enrique Delgado Ávila, Francesco Ballarin, Gianluigi Rozza
 Finite volumebased reduced order models for laminar flows / Matteo Zancanaro, Saddam Hijazi, Umberto Morelli, Giovanni Stabile, Gianluigi Rozza
 Finite volumebased reduced order models for turbulent flows / Matteo Zancanaro, Saddam Hijazi, Michele Girfoglio, Andrea Mola, Giovanni Stabile, Gianluigi Rozza
 Nonintrusive datadriven reduced order models in computational fluid dynamics / Marco Tezzele, Nicola Demo, Giovanni Stabile, Gianluigi Rozza
 Spectral element methodbased model order reduction / Martin W. Hess, Gianluigi Rozza
 Discontinuous Galerkinbased reduced order models / Andrea Lario, Francesco Romor, Gianluigi Rozza
 Weighted reduced order methods for uncertainty quantification / Davide Torlo, Maria Strazzullo, Francesco Ballarin, Gianluigi Rozza
 Reduced basis, embedded methods, and parametrized levelset geometry / Efthymios N. Karatzas, Giovanni Stabile, Francesco Ballarin, Gianluigi Rozza
 Reduced order methods for fluidstructure interaction problems / Monica Nonino, Francesco Ballarin, Gianluigi Rozza
 Reduced order models for bifurcating phenomena in fluidstructure interaction problems / Moaad Khamlich, Federico Pichi, Gianluigi Rozza
 Reduction in parameter space / Marco Tezzele, Francesco Romor, Gianluigi Rozza
 Geometrical parametrization and morphing techniques with applications / Andrea Mola, Nicola Demo, Marco Tezzele, Gianluigi Rozza
 Reduced order methods for hemodynamics applications / Zakia Zainib, Pierfrancesco Siena, Michele Girfoglio, Martin W. Hess, Francesco Ballarin, Gianluigi Rozza
 Scientific software development and packages for reduced order models in computational fluid dynamics / Nicola Demo, Marco Tezzele, Giovanni Stabile, Gianluigi Rozza
 A deep learning approach to improving reduced order models / Laura Meneghetti, Nirav Shah, Michele Girfoglio, Nicola Demo, Marco Tezzele, Andrea Lario, Giovanni Stabile, Gianluigi Rozza
11. Getting started with xPC target 3. [2006]
 Natick, Mass. : The MathWorks, 2006.
 Description
 Book — 1 v. (various pagings) ; 23 cm.
 Online
SAL3 (offcampus storage)
SAL3 (offcampus storage)  Status 

Stacks  Request (opens in new tab) 
QA76.9 .E96 A66 2006  Available 
 Veiga, Sébastien da, author.
 Philadelphia, Pennsylvania : Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104) : Mathematical Optimization Society, [2021]
 Description
 Book — 1 PDF (xvi, 291 pages)
 Summary

 A first look at screening using R
 Variancebased sensitivity measures
 Spectral and metamodelbased estimation
 Variancebased sensitivity measures with dependent inputs
 Beyond variancebased indices
 A case study in R : COVID19 epidemic model
(source: Nielsen Book Data)
 Washington, D.C. : United States. Dept. of Energy. Office of Science ; Oak Ridge, Tenn. : distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2012
 Description
 Book — 1 online resource.
 Summary

One of the physics goals for ITER is to achieve high fusion power PDT at a high gain QDT. This goal is important for studying the physics of reactorrelevant burning plasmas. Simulations of plasma performance in ITER can help achieve this goal by aiding in the design of systems such as diagnostics and in planning ITER plasma regimes. Simulations can indicate areas where further research in theory and experiments is needed. To have credible simulations integrated modeling is necessary since plasma profiles and applied heating, torque, and current drive are strongly coupled.
 Online
14. Variational Integration for Ideal MHD with Builtin Advection Equations [electronic resource]. [2014]
 Washington, D.C. : United States. Dept. of Energy. Office of Science ; Oak Ridge, Tenn. : distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2014
 Description
 Book — 1 online resource (11 p. ) : digital, PDF file.
 Summary

Newcomb's Lagrangian for ideal MHD in Lagrangian labeling is discretized using discrete exterior calculus. Variational integrators for ideal MHD are derived thereafter. Besides being symplectic and momentum preserving, the schemes inherit builtin advection equations from Newcomb's formulation, and therefore avoid solving them and the accompanying error and dissipation. We implement the method in 2D and show that numerical reconnection does not take place when singular current sheets are present. We then apply it to studying the dynamics of the ideal coalescence instability with multiple islands. The relaxed equilibrium state with embedded current sheets is obtained numerically.
 Online
 Wüthrich, Hansjürg.
 1., aktual. und erg. Aufl.  Morschen : SkriptoriumVerl., 2007.
 Description
 Book — 222 p. : Ill., port. 21 cm.
 Online
 Washington, D.C. : United States. Dept. of Energy. Office of Science ; Oak Ridge, Tenn. : distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2004
 Description
 Book — 1 online resource (768 Kilobytes pages ) : digital, PDF file.
 Summary

The M3D code has been using linear finite elements to represent multilevel MHD on 2D poloidal planes. Triangular higher order elements, up to third order, are constructed here in order to provide M3D the capability to solve highly anisotropic transport problems. It is found that higher order elements are essential to resolve the thin transition layer characteristic of the anisotropic transport equation, particularly when the strong anisotropic direction is not aligned with one of the Cartesian coordinates. The transition layer is measured by the profile width, which is zero for infinite anisotropy. It is shown that only higher order schemes have the ability to make this layer converge towards zero when the anisotropy gets stronger and stronger. Two cases are considered. One has the strong transport direction partially aligned with one of the element edges, the other doesn't have any alignment. Both cases have the strong transport direction misaligned with the grid line by some angles.
 Online
 Washington, D.C. : United States. Dept. of Energy. Office of Science ; Oak Ridge, Tenn. : distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2003
 Description
 Book — 1 online resource (5.2 MB pages ) : digital, PDF file.
 Summary

Recent Hmode experiments on Alcator CMod [I.H. Hutchinson, et al., Phys. Plasmas 1 (1994) 1511] which exhibit an internal transport barrier (ITB), have been examined with flux tube geometry gyrokinetic simulations, using the massively parallel code GS2 [M. Kotschenreuther, G. Rewoldt, and W.M. Tang, Comput. Phys. Commun. 88 (1995) 128]. The simulations support the picture of ion/electron temperature gradient (ITG/ETG) microturbulence driving high xi/ xe and that suppressed ITG causes reduced particle transport and improved ci on CMod. Nonlinear calculations for CMod confirm initial linear simulations, which predicted ITG stability in the barrier region just before ITB formation, without invoking E x B shear suppression of turbulence. Nonlinear fluxes are compared to experiment, which both show low heat transport in the ITB and higher transport within and outside of the barrier region.
 Online
 Oak Ridge, Tenn. : distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2003
 Description
 Book — 1 online resource (4.2 MB pages ) : digital, PDF file.
 Summary

We have developed a threaded parallel data streaming approach using Globus to transfer multiterabyte simulation data from a remote supercomputer to the scientist's home analysis/visualization cluster, as the simulation executes, with negligible overhead. Data transfer experiments show that this concurrent data transfer approach is more favorable compared with writing to local disk and then transferring this data to be postprocessed. The present approach is conducive to using the grid to pipeline the simulation with postprocessing and visualization. We have applied this method to the Gyrokinetic Toroidal Code (GTC), a 3dimensional particleincell code used to study microturbulence in magnetic confinement fusion from first principles plasma theory.
 Online
 Washington, D.C. : United States. Dept. of Energy. ; Oak Ridge, Tenn. : distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2010
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 Book — 1 online resource (821Kb ) : digital, PDF file.
 Summary

Implicit algorithms are essential for predicting the slow growth and saturation of global instabilities in today’s magnetically confined fusion plasma experiments. Present day algorithms for obtaining implicit solutions to the magnetohydrodynamic (MHD) equations for highly magnetized plasma have their roots in algorithms used in the 1960s and 1970s. However, today’s computers and modern linear and non‐linear solver techniques make practical much more comprehensive implicit algorithms than were previously possible. Combining these advanced implicit algorithms with highly accurate spatial representations of the vector fields describing the plasma flow and magnetic fields and with improved methods of calculating anisotropic thermal conduction now makes possible simulations of fusion experiments using realistic values of plasma parameters and actual configuration geometry.
 Online
 Washington, D.C. : United States. Dept. of Energy. Office of Science ; Oak Ridge, Tenn. : distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2005
 Description
 Book — 1 online resource (17.6 MB pages ) : digital, PDF file.
 Summary

Scientific simulation, which provides a natural bridge between theory and experiment, is an essential tool for understanding complex plasma behavior. Recent advances in simulations of magneticallyconfined plasmas are reviewed in this paper with illustrative examples chosen from associated research areas such as microturbulence, magnetohydrodynamics, and other topics. Progress has been stimulated in particular by the exponential growth of computer speed along with significant improvements in computer technology.
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