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Handbook of Heuristics / Rafael Martí, Panos Pardalos, Mauricio G. C. Resende editors
| Handbook of Heuristics / Rafael Martí, Panos Pardalos, Mauricio G. C. Resende editors |
| Edizione | [Continuously updated edition] |
| Pubbl/distr/stampa | Cham, : Springer, 2019- |
| Descrizione fisica | pag. varia : ill. ; 24 cm |
| Soggetto topico |
68-XX - Computer science [MSC 2020]
00A05 - Mathematics in general [MSC 2020] 00A69 - General applied mathematics [MSC 2020] 90-XX - Operations research, mathematical programming [MSC 2020] 90C59 - Approximation methods and heuristics in mathematical programming [MSC 2020] |
| Soggetto non controllato |
Algorithms
Analysis Math Applications in Computer Science Mathematical and Computational Engineering Mathematical software Optimization |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0127508 |
| Cham, : Springer, 2019- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Handbook of Heuristics / Rafael Martí, Panos Pardalos, Mauricio G. C. Resende editors
| Handbook of Heuristics / Rafael Martí, Panos Pardalos, Mauricio G. C. Resende editors |
| Edizione | [Continuously updated edition] |
| Pubbl/distr/stampa | Cham, : Springer, 2019- |
| Descrizione fisica | pag. varia : ill. ; 24 cm |
| Soggetto topico |
00A05 - Mathematics in general [MSC 2020]
00A69 - General applied mathematics [MSC 2020] 68-XX - Computer science [MSC 2020] 90-XX - Operations research, mathematical programming [MSC 2020] 90C59 - Approximation methods and heuristics in mathematical programming [MSC 2020] |
| Soggetto non controllato |
Algorithms
Analysis Math Applications in Computer Science Mathematical and Computational Engineering Mathematical software Optimization |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00127508 |
| Cham, : Springer, 2019- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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International Symposium on Mathematics, Quantum Theory, and Cryptography [[electronic resource] ] : Proceedings of MQC 2019 / / edited by Tsuyoshi Takagi, Masato Wakayama, Keisuke Tanaka, Noboru Kunihiro, Kazufumi Kimoto, Yasuhiko Ikematsu
| International Symposium on Mathematics, Quantum Theory, and Cryptography [[electronic resource] ] : Proceedings of MQC 2019 / / edited by Tsuyoshi Takagi, Masato Wakayama, Keisuke Tanaka, Noboru Kunihiro, Kazufumi Kimoto, Yasuhiko Ikematsu |
| Autore | Takagi Tsuyoshi |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Springer Nature, 2021 |
| Descrizione fisica | 1 online resource (XII, 274 p. 83 illus., 24 illus. in color.) |
| Disciplina | 519 |
| Collana | Mathematics for Industry |
| Soggetto topico |
Applied mathematics
Engineering mathematics Data structures (Computer science) Quantum computers Computer security Mathematical and Computational Engineering Data Structures and Information Theory Quantum Computing Systems and Data Security |
| Soggetto non controllato |
Mathematical and Computational Engineering
Data Structures and Information Theory Quantum Computing Systems and Data Security Mathematical and Computational Engineering Applications Data and Information Security Cryptography for Quantum Computers Post-quantum Cryptography Number Theory Representation Theory Quantum Physics Security Modelling Open Access Maths for engineers Algorithms & data structures Information theory Mathematical theory of computation Computer security Network security |
| ISBN | 981-15-5191-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Sustainable Cryptography -- What Kind of Insight Provide Analytical Solutions of Quantum Models? -- Emerging Ultrastrong Coupling between Light and Matter Observed in Circuit Quantum Electrodynamics -- Quantum Random Numbers Generated by a Cloud Superconducting Quantum Computer -- Quantum Factoring Algorithm: Resource Estimation and Survey of Experiments -- A Review of Secret Key Distribution Based on Bounded Observability -- Towards Constructing Fully Homomorphic Encryption without Ciphertext Noise from Group Theory -- Number Theoretic Study in Quantum Interactions -- From the Bloch Sphere to Phase Space Representations with the Gottesman-Kitaev-Preskill Encoding -- A Data Concealing Technique with Random Noise Disturbance and A Restoring Technique for the Concealed Data by Stochastic Process Estimation. |
| Record Nr. | UNISA-996466557703316 |
Takagi Tsuyoshi
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| Springer Nature, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Pedestrian and evacuation dynamics 2012 / Ulrich Weidmann, Uwe Kirsch, Michael Schreckenberg editors
| Pedestrian and evacuation dynamics 2012 / Ulrich Weidmann, Uwe Kirsch, Michael Schreckenberg editors |
| Pubbl/distr/stampa | Cham, : Springer, 2014 |
| Descrizione fisica | XXIV, 1424 p. : ill. ; 24 cm |
| Soggetto topico | 97Mxx - Education of mathematical modeling and applications of mathematics [MSC 2020] |
| Soggetto non controllato |
Civil Engineering
Computational science and engineering Decision theory Mathematical and Computational Engineering Numeric Computing Operations Research Simulation and Modeling |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0103242 |
| Cham, : Springer, 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Pedestrian and evacuation dynamics 2012 / Ulrich Weidmann, Uwe Kirsch, Michael Schreckenberg editors
| Pedestrian and evacuation dynamics 2012 / Ulrich Weidmann, Uwe Kirsch, Michael Schreckenberg editors |
| Pubbl/distr/stampa | Cham, : Springer, 2014 |
| Descrizione fisica | XXIV, 1424 p. : ill. ; 24 cm |
| Soggetto topico | 97Mxx - Education of mathematical modeling and applications of mathematics [MSC 2020] |
| Soggetto non controllato |
Civil Engineering
Computational science and engineering Decision theory Mathematical and Computational Engineering Numeric Computing Operations Research Simulation and Modeling |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00103242 |
| Cham, : Springer, 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Probability in Electrical Engineering and Computer Science [[electronic resource] ] : An Application-Driven Course
| Probability in Electrical Engineering and Computer Science [[electronic resource] ] : An Application-Driven Course |
| Autore | Walrand Jean |
| Pubbl/distr/stampa | Cham, : Springer International Publishing AG, 2021 |
| Descrizione fisica | 1 online resource (390 p.) |
| Soggetto topico |
Maths for computer scientists
Communications engineering / telecommunications Maths for engineers Probability & statistics |
| Soggetto non controllato |
Probability and Statistics in Computer Science
Communications Engineering, Networks Mathematical and Computational Engineering Probability Theory and Stochastic Processes Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Mathematical and Computational Engineering Applications Probability Theory Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences Applied probability Hypothesis testing Detection theory Expectation maximization Stochastic dynamic programming Machine learning Stochastic gradient descent Deep neural networks Matrix completion Linear and polynomial regression Open Access Maths for computer scientists Mathematical & statistical software Communications engineering / telecommunications Maths for engineers Probability & statistics Stochastics |
| ISBN | 3-030-49995-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996464521903316 |
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Walrand Jean
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| Cham, : Springer International Publishing AG, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Probability in Electrical Engineering and Computer Science : An Application-Driven Course
| Probability in Electrical Engineering and Computer Science : An Application-Driven Course |
| Autore | Walrand Jean |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham, : Springer International Publishing AG, 2021 |
| Descrizione fisica | 1 online resource (390 p.) |
| Soggetto topico |
Maths for computer scientists
Communications engineering / telecommunications Maths for engineers Probability & statistics |
| Soggetto non controllato |
Probability and Statistics in Computer Science
Communications Engineering, Networks Mathematical and Computational Engineering Probability Theory and Stochastic Processes Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Mathematical and Computational Engineering Applications Probability Theory Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences Applied probability Hypothesis testing Detection theory Expectation maximization Stochastic dynamic programming Machine learning Stochastic gradient descent Deep neural networks Matrix completion Linear and polynomial regression Open Access Maths for computer scientists Mathematical & statistical software Communications engineering / telecommunications Maths for engineers Probability & statistics Stochastics |
| ISBN | 3-030-49995-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Introduction -- About This Second Edition -- Contents -- 1 PageRank: A -- 1.1 Model -- 1.2 Markov Chain -- 1.2.1 General Definition -- 1.2.2 Distribution After n Steps and Invariant Distribution -- 1.3 Analysis -- 1.3.1 Irreducibility and Aperiodicity -- 1.3.2 Big Theorem -- 1.3.3 Long-Term Fraction of Time -- 1.4 Illustrations -- 1.5 Hitting Time -- 1.5.1 Mean Hitting Time -- 1.5.2 Probability of Hitting a State Before Another -- 1.5.3 FSE for Markov Chain -- 1.6 Summary -- 1.6.1 Key Equations and Formulas -- 1.7 References -- 1.8 Problems -- 2 PageRank: B -- 2.1 Sample Space -- 2.2 Laws of Large Numbers for Coin Flips -- 2.2.1 Convergence in Probability -- 2.2.2 Almost Sure Convergence -- 2.3 Laws of Large Numbers for i.i.d. RVs -- 2.3.1 Weak Law of Large Numbers -- 2.3.2 Strong Law of Large Numbers -- 2.4 Law of Large Numbers for Markov Chains -- 2.5 Proof of Big Theorem -- 2.5.1 Proof of Theorem 1.1 (a) -- 2.5.2 Proof of Theorem 1.1 (b) -- 2.5.3 Periodicity -- 2.6 Summary -- 2.6.1 Key Equations and Formulas -- 2.7 References -- 2.8 Problems -- 3 Multiplexing: A -- 3.1 Sharing Links -- 3.2 Gaussian Random Variable and CLT -- 3.2.1 Binomial and Gaussian -- 3.2.2 Multiplexing and Gaussian -- 3.2.3 Confidence Intervals -- 3.3 Buffers -- 3.3.1 Markov Chain Model of Buffer -- 3.3.2 Invariant Distribution -- 3.3.3 Average Delay -- 3.3.4 A Note About Arrivals -- 3.3.5 Little's Law -- 3.4 Multiple Access -- 3.5 Summary -- 3.5.1 Key Equations and Formulas -- 3.6 References -- 3.7 Problems -- 4 Multiplexing: B -- 4.1 Characteristic Functions -- 4.2 Proof of CLT (Sketch) -- 4.3 Moments of N(0, 1) -- 4.4 Sum of Squares of 2 i.i.d. N(0, 1) -- 4.5 Two Applications of Characteristic Functions -- 4.5.1 Poisson as a Limit of Binomial -- 4.5.2 Exponential as Limit of Geometric -- 4.6 Error Function.
4.7 Adaptive Multiple Access -- 4.8 Summary -- 4.8.1 Key Equations and Formulas -- 4.9 References -- 4.10 Problems -- 5 Networks: A -- 5.1 Spreading Rumors -- 5.2 Cascades -- 5.3 Seeding the Market -- 5.4 Manufacturing of Consent -- 5.5 Polarization -- 5.6 M/M/1 Queue -- 5.7 Network of Queues -- 5.8 Optimizing Capacity -- 5.9 Internet and Network of Queues -- 5.10 Product-Form Networks -- 5.10.1 Example -- 5.11 References -- 5.12 Problems -- 6 Networks-B -- 6.1 Social Networks -- 6.2 Continuous-Time Markov Chains -- 6.2.1 Two-State Markov Chain -- 6.2.2 Three-State Markov Chain -- 6.2.3 General Case -- 6.2.4 Uniformization -- 6.2.5 Time Reversal -- 6.3 Product-Form Networks -- 6.4 Proof of Theorem 5.7 -- 6.5 References -- 7 Digital Link-A -- 7.1 Digital Link -- 7.2 Detection and Bayes' Rule -- 7.2.1 Bayes' Rule -- 7.2.2 Circumstances vs. Causes -- 7.2.3 MAP and MLE -- Example: Ice Cream and Sunburn -- 7.2.4 Binary Symmetric Channel -- 7.3 Huffman Codes -- 7.4 Gaussian Channel -- Simulation -- 7.4.1 BPSK -- 7.5 Multidimensional Gaussian Channel -- 7.5.1 MLE in Multidimensional Case -- 7.6 Hypothesis Testing -- 7.6.1 Formulation -- 7.6.2 Solution -- 7.6.3 Examples -- Gaussian Channel -- Mean of Exponential RVs -- Bias of a Coin -- Discrete Observations -- 7.7 Summary -- 7.7.1 Key Equations and Formulas -- 7.8 References -- 7.9 Problems -- 8 Digital Link-B -- 8.1 Proof of Optimality of the Huffman Code -- 8.2 Proof of Neyman-Pearson Theorem 7.4 -- 8.3 Jointly Gaussian Random Variables -- 8.3.1 Density of Jointly Gaussian Random Variables -- 8.4 Elementary Statistics -- 8.4.1 Zero-Mean? -- 8.4.2 Unknown Variance -- 8.4.3 Difference of Means -- 8.4.4 Mean in Hyperplane? -- 8.4.5 ANOVA -- 8.5 LDPC Codes -- 8.6 Summary -- 8.6.1 Key Equations and Formulas -- 8.7 References -- 8.8 Problems -- 9 Tracking-A -- 9.1 Examples -- 9.2 Estimation Problem. 9.3 Linear Least Squares Estimates -- 9.3.1 Projection -- 9.4 Linear Regression -- 9.5 A Note on Overfitting -- 9.6 MMSE -- 9.6.1 MMSE for Jointly Gaussian -- 9.7 Vector Case -- 9.8 Kalman Filter -- 9.8.1 The Filter -- 9.8.2 Examples -- Random Walk -- Random Walk with Unknown Drift -- Random Walk with Changing Drift -- Falling Object -- 9.9 Summary -- 9.9.1 Key Equations and Formulas -- 9.10 References -- 9.11 Problems -- 10 Tracking: B -- 10.1 Updating LLSE -- 10.2 Derivation of Kalman Filter -- 10.3 Properties of Kalman Filter -- 10.3.1 Observability -- 10.3.2 Reachability -- 10.4 Extended Kalman Filter -- 10.4.1 Examples -- 10.5 Summary -- 10.5.1 Key Equations and Formulas -- 10.6 References -- 11 Speech Recognition: A -- 11.1 Learning: Concepts and Examples -- 11.2 Hidden Markov Chain -- 11.3 Expectation Maximization and Clustering -- 11.3.1 A Simple Clustering Problem -- 11.3.2 A Second Look -- 11.4 Learning: Hidden Markov Chain -- 11.4.1 HEM -- 11.4.2 Training the Viterbi Algorithm -- 11.5 Summary -- 11.5.1 Key Equations and Formulas -- 11.6 References -- 11.7 Problems -- 12 Speech Recognition: B -- 12.1 Online Linear Regression -- 12.2 Theory of Stochastic Gradient Projection -- 12.2.1 Gradient Projection -- 12.2.2 Stochastic Gradient Projection -- 12.2.3 Martingale Convergence -- 12.3 Big Data -- 12.3.1 Relevant Data -- 12.3.2 Compressed Sensing -- 12.3.3 Recommendation Systems -- 12.4 Deep Neural Networks -- 12.4.1 Calculating Derivatives -- 12.5 Summary -- 12.5.1 Key Equations and Formulas -- 12.6 References -- 12.7 Problems -- 13 Route Planning: A -- 13.1 Model -- 13.2 Formulation 1: Pre-planning -- 13.3 Formulation 2: Adapting -- 13.4 Markov Decision Problem -- 13.4.1 Examples -- 13.5 Infinite Horizon -- 13.6 Summary -- 13.6.1 Key Equations and Formulas -- 13.7 References -- 13.8 Problems -- 14 Route Planning: B -- 14.1 LQG Control. 14.1.1 Letting N →∞ -- 14.2 LQG with Noisy Observations -- 14.2.1 Letting N →∞ -- 14.3 Partially Observed MDP -- 14.3.1 Example: Searching for Your Keys -- 14.4 Summary -- 14.4.1 Key Equations and Formulas -- 14.5 References -- 14.6 Problems -- 15 Perspective and Complements -- 15.1 Inference -- 15.2 Sufficient Statistic -- 15.2.1 Interpretation -- 15.3 Infinite Markov Chains -- 15.3.1 Lyapunov-Foster Criterion -- 15.4 Poisson Process -- 15.4.1 Definition -- 15.4.2 Independent Increments -- 15.4.3 Number of Jumps -- 15.5 Boosting -- 15.6 Multi-Armed Bandits -- 15.7 Capacity of BSC -- 15.8 Bounds on Probabilities -- 15.8.1 Applying the Bounds to Multiplexing -- 15.9 Martingales -- 15.9.1 Definitions -- 15.9.2 Examples -- 15.9.3 Law of Large Numbers -- 15.9.4 Wald's Equality -- 15.10 Summary -- 15.10.1 Key Equations and Formulas -- 15.11 References -- 15.12 Problems -- Correction to: Probability in Electrical Engineering and Computer Science -- Correction to: Probability in Electrical Engineering and Computer Science (Funding Information) -- A Elementary Probability -- A.1 Symmetry -- A.2 Conditioning -- A.3 Common Confusion -- A.4 Independence -- A.5 Expectation -- A.6 Variance -- A.7 Inequalities -- A.8 Law of Large Numbers -- A.9 Covariance and Regression -- A.10 Why Do We Need a More Sophisticated Formalism? -- A.11 References -- A.12 Solved Problems -- B Basic Probability -- B.1 General Framework -- B.1.1 Probability Space -- B.1.2 Borel-Cantelli Theorem -- B.1.3 Independence -- B.1.4 Converse of Borel-Cantelli Theorem -- B.1.5 Conditional Probability -- B.1.6 Random Variable -- B.2 Discrete Random Variable -- B.2.1 Definition -- B.2.2 Expectation -- B.2.3 Function of a RV -- B.2.4 Nonnegative RV -- B.2.5 Linearity of Expectation -- B.2.6 Monotonicity of Expectation -- B.2.7 Variance, Standard Deviation. B.2.8 Important Discrete Random Variables -- B.3 Multiple Discrete Random Variables -- B.3.1 Joint Distribution -- B.3.2 Independence -- B.3.3 Expectation of Function of Multiple RVs -- B.3.4 Covariance -- B.3.5 Conditional Expectation -- B.3.6 Conditional Expectation of a Function -- B.4 General Random Variables -- B.4.1 Definitions -- B.4.2 Examples -- B.4.3 Expectation -- B.4.4 Continuity of Expectation -- B.5 Multiple Random Variables -- B.5.1 Random Vector -- B.5.2 Minimum and Maximum of Independent RVs -- B.5.3 Sum of Independent Random Variables -- B.6 Random Vectors -- B.6.1 Orthogonality and Projection -- B.7 Density of a Function of Random Variables -- B.7.1 Linear Transformations -- B.7.2 Nonlinear Transformations -- B.8 References -- B.9 Problems -- References -- Index. |
| Record Nr. | UNINA-9910488709003321 |
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Walrand Jean
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| Cham, : Springer International Publishing AG, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Software for Exascale Computing - SPPEXA 2016-2019 [[electronic resource] /] / edited by Hans-Joachim Bungartz, Severin Reiz, Benjamin Uekermann, Philipp Neumann, Wolfgang E. Nagel
| Software for Exascale Computing - SPPEXA 2016-2019 [[electronic resource] /] / edited by Hans-Joachim Bungartz, Severin Reiz, Benjamin Uekermann, Philipp Neumann, Wolfgang E. Nagel |
| Autore | Bungartz Hans-Joachim |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Springer Nature, 2020 |
| Descrizione fisica | 1 online resource (XII, 620 p. 256 illus., 231 illus. in color.) |
| Disciplina | 003.3 |
| Collana | Lecture Notes in Computational Science and Engineering |
| Soggetto topico |
Computer simulation
Computer software—Reusability Computer mathematics Input-output equipment (Computers) Applied mathematics Engineering mathematics Physics Simulation and Modeling Performance and Reliability Computational Science and Engineering Input/Output and Data Communications Mathematical and Computational Engineering Numerical and Computational Physics, Simulation |
| Soggetto non controllato |
Simulation and Modeling
Performance and Reliability Computational Science and Engineering Input/Output and Data Communications Mathematical and Computational Engineering Numerical and Computational Physics, Simulation Computer Science Computer Hardware Mathematical and Computational Engineering Applications Theoretical, Mathematical and Computational Physics open access computational algorithms and numerical methods data management and exploration high-performance computing simulation software and applications system software and software tools Computer modelling & simulation Systems analysis & design Maintenance & repairs Maths for scientists Computer networking & communications Distributed databases Maths for engineers Mathematical physics |
| ISBN | 3-030-47956-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | EXA-DUNE: Flexible PDE Solvers, Numerical Methods, and Applications -- Smart-DASH: Smart Data Structures and Algorithms with Support for Hierarchical Locality -- Terra-Neo: Integrated Co-Design of an Exascale Earth Mantle Modeling Framework -- EXASTEEL-2: Dual Phase Steels - from Micro to Macro Properties -- GROMEX: Unified Long-range Electrostatics and Dynamic Protonation for Realistic Biomolecular Simulations on the Exascale -- ExaStencils: Advanced Stencil-Code Engineering -- ExaFSA: Exascale Simulation of Fluid-Structure-Acoustics Interactions -- EXAHD: An Exa-Scalable Two-Level Sparse Grid Approach for Higher-Dimensional Problems in Plasma Physics and Beyond -- EXAMAG: Exascale Simulations of the Magnetic Universe -- FFMK: A Fast and Fault Tolerant Microkernel-based System for Exascale Computing -- ESSEX-II: Equipping Sparse Solvers for Exascale -- EXASOLVERS: Extreme Scale Solvers for Coupled Problems -- ADA-FS: Advanced Data Placement via Ad-hoc File Systems at Extreme Scales -- AIMES: Advanced Computation and I/O Methods for Earth-System Simulations. ExaDG: High-Order Discontinuous Galerkin for the Exa-Scale. MYX-MUST Correctness Checking for YML and XMP Programs -- ExtraPeak: Automatic Performance Modeling of HPC Applications with Multiple Model Parameters. |
| Record Nr. | UNISA-996418265803316 |
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Bungartz Hans-Joachim
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| Springer Nature, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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