High-dimensional nonlinear diffusion stochastic processes :: modelling for engineering applications /
Annotation This book is one of the first few devoted to high-dimensional diffusion stochastic processes with nonlinear coefficients. These processes are closely associated with large systems of Ito's stochastic differential equations and with discretized-in-the-parameter versions of Ito's...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore ; River Edge, NJ :
World Scientific,
2001.
|
| Schriftenreihe: | Series on advances in mathematics for applied sciences ;
v. 56. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Annotation This book is one of the first few devoted to high-dimensional diffusion stochastic processes with nonlinear coefficients. These processes are closely associated with large systems of Ito's stochastic differential equations and with discretized-in-the-parameter versions of Ito's stochastic differential equations that are nonlocally dependent on the parameter. The latter models include Ito's stochastic integro-differential, partial differential and partial integro-differential equations. The book presents the new analytical treatment which can serve as the basis of a combined, analytical -- numerical approach to greater computational efficiency. Some examples of the modelling of noise in semiconductor devices are provided. |
| Beschreibung: | 1 online resource (xviii, 297 pages) |
| Bibliographie: | Includes bibliographical references (and index. |
| ISBN: | 9789812810540 9812810544 |
Internformat
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| 100 | 1 | |a Mamontov, Yevgeny, |d 1955- |1 https://id.oclc.org/worldcat/entity/E39PCjDbTbpbbBgGWPTJkHXwyb |0 http://id.loc.gov/authorities/names/n00006062 | |
| 245 | 1 | 0 | |a High-dimensional nonlinear diffusion stochastic processes : |b modelling for engineering applications / |c Yevgeny Mamontov, Magnus Willander. |
| 260 | |a Singapore ; |a River Edge, NJ : |b World Scientific, |c 2001. | ||
| 300 | |a 1 online resource (xviii, 297 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 490 | 1 | |a Series on advances in mathematics for applied sciences ; |v v. 56 | |
| 504 | |a Includes bibliographical references (and index. | ||
| 588 | 0 | |a Print version record. | |
| 520 | 8 | |a Annotation This book is one of the first few devoted to high-dimensional diffusion stochastic processes with nonlinear coefficients. These processes are closely associated with large systems of Ito's stochastic differential equations and with discretized-in-the-parameter versions of Ito's stochastic differential equations that are nonlocally dependent on the parameter. The latter models include Ito's stochastic integro-differential, partial differential and partial integro-differential equations. The book presents the new analytical treatment which can serve as the basis of a combined, analytical -- numerical approach to greater computational efficiency. Some examples of the modelling of noise in semiconductor devices are provided. | |
| 505 | 0 | |a Preface; Contents; Chapter 1 Introductory Chapter; 1.1 Prerequisites for Reading; 1.2 Random Variable. Stochastic Process. Random Field. High-Dimensional Process. One-Point Process; 1.3 Two-Point Process. Expectation. Markov Process. Example of Non-Markov Process Associated with Multidimensional Markov Process; 1.4 Preceding Subsequent and Transition Probability Densities. The Chapman-Kolmogorov Equation. Initial Condition for Markov Process; 1.4.1 The Chapman-Kolmogorov equation; 1.4.2 Initial condition for Markov process. | |
| 505 | 8 | |a 1.5 Homogeneous Markov Process. Example of Markov Process: The Wiener Process1.6 Expectation Variance and Standard Deviations of Markov Process; 1.7 Invariant and Stationary Markov Processes. Covariance. Spectral Densities; 1.8 Diffusion Process; 1.9 Example of Diffusion Processes: Solutions of Ito's Stochastic Ordinary Differential Equation; 1.10 The Kolmogorov Backward Equation; 1.11 Figures of Merit. Diffusion Modelling of High-Dimensional Systems; 1.12 Common Analytical Techniques to Determine Probability Densities of Diffusion Processes. The Kolmogorov Forward Equation. | |
| 505 | 8 | |a 1.12.1 Probability density1.12.2 Invariant probability density; 1.12.3 Stationary probability density; 1.13 The Purpose and Content of This Book; Chapter 2 Diffusion Processes; 2.1 Introduction; 2.2 Time-Derivatives of Expectation and Variance; 2.3 Ordinary Differential Equation Systems for Expectation; 2.3.1 The first-order system; 2.3.2 The second-order system; 2.3.3 Systems of the higher orders; 2.4 Models for Noise-Induced Phenomena in Expectation; 2.4.1 The case of stochastic resonance; 2.4.2 Practically efficient implementation of the second-order system. | |
| 505 | 8 | |a 2.5 Ordinary Differential Equation System for Variance2.5.1 Damping matrix; 2.5.2 The uncorrelated-matrixes approximation; 2.5.3 Nonlinearity of the drift function; 2.5.4 Fundamental limitation of the state-space-independent approximations for the diffusion and damping matrixes; 2.6 The Steady-State Approximation for The Probability Density; Chapter 3 Invariant Diffusion Processes; 3.1 Introduction; 3.2 Preliminary Remarks; 3.3 Expectation. The Finite-Equation Method; 3.4 Explicit Expression for Variance; 3.5 The Simplified Detailed-Balance Approximation for Invariant Probability Density. | |
| 505 | 8 | |a 3.5.1 Partial differential equation for logarithm of the density3.5.2 Truncated equation for the logarithm and the detailed-balance equation; 3.5.3 Case of the detailed balance; 3.5.4 The detailed-balance approximation; 3.5.5 The simplified detailed-balance approximation. Theorem on the approximating density; 3.6 Analytical-Numerical Approach to Non-Invariant and Invariant Diffusion Processes; 3.6.1 Choice of the bounded domain of the integration; 3.6.2 Evaluation of the multifold integrals. The Monte Carlo technique; 3.6.3 Summary of the approach; 3.7 Discussion. | |
| 650 | 0 | |a Engineering |x Mathematical models. | |
| 650 | 0 | |a Stochastic processes. |0 http://id.loc.gov/authorities/subjects/sh85128181 | |
| 650 | 0 | |a Diffusion processes. |0 http://id.loc.gov/authorities/subjects/sh85037941 | |
| 650 | 0 | |a Differential equations, Nonlinear. |0 http://id.loc.gov/authorities/subjects/sh85037906 | |
| 650 | 2 | |a Stochastic Processes |0 https://id.nlm.nih.gov/mesh/D013269 | |
| 650 | 6 | |a Ingénierie |x Modèles mathématiques. | |
| 650 | 6 | |a Processus stochastiques. | |
| 650 | 6 | |a Processus de diffusion. | |
| 650 | 6 | |a Équations différentielles non linéaires. | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Engineering (General) |2 bisacsh | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Reference. |2 bisacsh | |
| 650 | 7 | |a Differential equations, Nonlinear |2 fast | |
| 650 | 7 | |a Diffusion processes |2 fast | |
| 650 | 7 | |a Engineering |x Mathematical models |2 fast | |
| 650 | 7 | |a Stochastic processes |2 fast | |
| 700 | 1 | |a Willander, M. |1 https://id.oclc.org/worldcat/entity/E39PCjyMkmcMTJRpKKtdGWp8wd |0 http://id.loc.gov/authorities/names/n97015960 | |
| 758 | |i has work: |a High-dimensional nonlinear diffusion stochastic processes (Text) |1 https://id.oclc.org/worldcat/entity/E39PCH6dmX34dPQRRkrJdyGKBd |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
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| contents | Preface; Contents; Chapter 1 Introductory Chapter; 1.1 Prerequisites for Reading; 1.2 Random Variable. Stochastic Process. Random Field. High-Dimensional Process. One-Point Process; 1.3 Two-Point Process. Expectation. Markov Process. Example of Non-Markov Process Associated with Multidimensional Markov Process; 1.4 Preceding Subsequent and Transition Probability Densities. The Chapman-Kolmogorov Equation. Initial Condition for Markov Process; 1.4.1 The Chapman-Kolmogorov equation; 1.4.2 Initial condition for Markov process. 1.5 Homogeneous Markov Process. Example of Markov Process: The Wiener Process1.6 Expectation Variance and Standard Deviations of Markov Process; 1.7 Invariant and Stationary Markov Processes. Covariance. Spectral Densities; 1.8 Diffusion Process; 1.9 Example of Diffusion Processes: Solutions of Ito's Stochastic Ordinary Differential Equation; 1.10 The Kolmogorov Backward Equation; 1.11 Figures of Merit. Diffusion Modelling of High-Dimensional Systems; 1.12 Common Analytical Techniques to Determine Probability Densities of Diffusion Processes. The Kolmogorov Forward Equation. 1.12.1 Probability density1.12.2 Invariant probability density; 1.12.3 Stationary probability density; 1.13 The Purpose and Content of This Book; Chapter 2 Diffusion Processes; 2.1 Introduction; 2.2 Time-Derivatives of Expectation and Variance; 2.3 Ordinary Differential Equation Systems for Expectation; 2.3.1 The first-order system; 2.3.2 The second-order system; 2.3.3 Systems of the higher orders; 2.4 Models for Noise-Induced Phenomena in Expectation; 2.4.1 The case of stochastic resonance; 2.4.2 Practically efficient implementation of the second-order system. 2.5 Ordinary Differential Equation System for Variance2.5.1 Damping matrix; 2.5.2 The uncorrelated-matrixes approximation; 2.5.3 Nonlinearity of the drift function; 2.5.4 Fundamental limitation of the state-space-independent approximations for the diffusion and damping matrixes; 2.6 The Steady-State Approximation for The Probability Density; Chapter 3 Invariant Diffusion Processes; 3.1 Introduction; 3.2 Preliminary Remarks; 3.3 Expectation. The Finite-Equation Method; 3.4 Explicit Expression for Variance; 3.5 The Simplified Detailed-Balance Approximation for Invariant Probability Density. 3.5.1 Partial differential equation for logarithm of the density3.5.2 Truncated equation for the logarithm and the detailed-balance equation; 3.5.3 Case of the detailed balance; 3.5.4 The detailed-balance approximation; 3.5.5 The simplified detailed-balance approximation. Theorem on the approximating density; 3.6 Analytical-Numerical Approach to Non-Invariant and Invariant Diffusion Processes; 3.6.1 Choice of the bounded domain of the integration; 3.6.2 Evaluation of the multifold integrals. The Monte Carlo technique; 3.6.3 Summary of the approach; 3.7 Discussion. |
| ctrlnum | (OCoLC)268966013 |
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"><subfield code="a">data file</subfield></datafield><datafield tag="490" ind1="1" ind2=" "><subfield code="a">Series on advances in mathematics for applied sciences ;</subfield><subfield code="v">v. 56</subfield></datafield><datafield tag="504" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references (and index.</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Print version record.</subfield></datafield><datafield tag="520" ind1="8" ind2=" "><subfield code="a">Annotation This book is one of the first few devoted to high-dimensional diffusion stochastic processes with nonlinear coefficients. 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Example of Non-Markov Process Associated with Multidimensional Markov Process; 1.4 Preceding Subsequent and Transition Probability Densities. The Chapman-Kolmogorov Equation. Initial Condition for Markov Process; 1.4.1 The Chapman-Kolmogorov equation; 1.4.2 Initial condition for Markov process.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">1.5 Homogeneous Markov Process. Example of Markov Process: The Wiener Process1.6 Expectation Variance and Standard Deviations of Markov Process; 1.7 Invariant and Stationary Markov Processes. Covariance. Spectral Densities; 1.8 Diffusion Process; 1.9 Example of Diffusion Processes: Solutions of Ito's Stochastic Ordinary Differential Equation; 1.10 The Kolmogorov Backward Equation; 1.11 Figures of Merit. Diffusion Modelling of High-Dimensional Systems; 1.12 Common Analytical Techniques to Determine Probability Densities of Diffusion Processes. 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| id | ZDB-4-EBA-ocn268966013 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:16:33Z |
| institution | BVB |
| isbn | 9789812810540 9812810544 |
| language | English |
| oclc_num | 268966013 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xviii, 297 pages) |
| psigel | ZDB-4-EBA |
| publishDate | 2001 |
| publishDateSearch | 2001 |
| publishDateSort | 2001 |
| publisher | World Scientific, |
| record_format | marc |
| series | Series on advances in mathematics for applied sciences ; |
| series2 | Series on advances in mathematics for applied sciences ; |
| spelling | Mamontov, Yevgeny, 1955- https://id.oclc.org/worldcat/entity/E39PCjDbTbpbbBgGWPTJkHXwyb http://id.loc.gov/authorities/names/n00006062 High-dimensional nonlinear diffusion stochastic processes : modelling for engineering applications / Yevgeny Mamontov, Magnus Willander. Singapore ; River Edge, NJ : World Scientific, 2001. 1 online resource (xviii, 297 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier data file Series on advances in mathematics for applied sciences ; v. 56 Includes bibliographical references (and index. Print version record. Annotation This book is one of the first few devoted to high-dimensional diffusion stochastic processes with nonlinear coefficients. These processes are closely associated with large systems of Ito's stochastic differential equations and with discretized-in-the-parameter versions of Ito's stochastic differential equations that are nonlocally dependent on the parameter. The latter models include Ito's stochastic integro-differential, partial differential and partial integro-differential equations. The book presents the new analytical treatment which can serve as the basis of a combined, analytical -- numerical approach to greater computational efficiency. Some examples of the modelling of noise in semiconductor devices are provided. Preface; Contents; Chapter 1 Introductory Chapter; 1.1 Prerequisites for Reading; 1.2 Random Variable. Stochastic Process. Random Field. High-Dimensional Process. One-Point Process; 1.3 Two-Point Process. Expectation. Markov Process. Example of Non-Markov Process Associated with Multidimensional Markov Process; 1.4 Preceding Subsequent and Transition Probability Densities. The Chapman-Kolmogorov Equation. Initial Condition for Markov Process; 1.4.1 The Chapman-Kolmogorov equation; 1.4.2 Initial condition for Markov process. 1.5 Homogeneous Markov Process. Example of Markov Process: The Wiener Process1.6 Expectation Variance and Standard Deviations of Markov Process; 1.7 Invariant and Stationary Markov Processes. Covariance. Spectral Densities; 1.8 Diffusion Process; 1.9 Example of Diffusion Processes: Solutions of Ito's Stochastic Ordinary Differential Equation; 1.10 The Kolmogorov Backward Equation; 1.11 Figures of Merit. Diffusion Modelling of High-Dimensional Systems; 1.12 Common Analytical Techniques to Determine Probability Densities of Diffusion Processes. The Kolmogorov Forward Equation. 1.12.1 Probability density1.12.2 Invariant probability density; 1.12.3 Stationary probability density; 1.13 The Purpose and Content of This Book; Chapter 2 Diffusion Processes; 2.1 Introduction; 2.2 Time-Derivatives of Expectation and Variance; 2.3 Ordinary Differential Equation Systems for Expectation; 2.3.1 The first-order system; 2.3.2 The second-order system; 2.3.3 Systems of the higher orders; 2.4 Models for Noise-Induced Phenomena in Expectation; 2.4.1 The case of stochastic resonance; 2.4.2 Practically efficient implementation of the second-order system. 2.5 Ordinary Differential Equation System for Variance2.5.1 Damping matrix; 2.5.2 The uncorrelated-matrixes approximation; 2.5.3 Nonlinearity of the drift function; 2.5.4 Fundamental limitation of the state-space-independent approximations for the diffusion and damping matrixes; 2.6 The Steady-State Approximation for The Probability Density; Chapter 3 Invariant Diffusion Processes; 3.1 Introduction; 3.2 Preliminary Remarks; 3.3 Expectation. The Finite-Equation Method; 3.4 Explicit Expression for Variance; 3.5 The Simplified Detailed-Balance Approximation for Invariant Probability Density. 3.5.1 Partial differential equation for logarithm of the density3.5.2 Truncated equation for the logarithm and the detailed-balance equation; 3.5.3 Case of the detailed balance; 3.5.4 The detailed-balance approximation; 3.5.5 The simplified detailed-balance approximation. Theorem on the approximating density; 3.6 Analytical-Numerical Approach to Non-Invariant and Invariant Diffusion Processes; 3.6.1 Choice of the bounded domain of the integration; 3.6.2 Evaluation of the multifold integrals. The Monte Carlo technique; 3.6.3 Summary of the approach; 3.7 Discussion. Engineering Mathematical models. Stochastic processes. http://id.loc.gov/authorities/subjects/sh85128181 Diffusion processes. http://id.loc.gov/authorities/subjects/sh85037941 Differential equations, Nonlinear. http://id.loc.gov/authorities/subjects/sh85037906 Stochastic Processes https://id.nlm.nih.gov/mesh/D013269 Ingénierie Modèles mathématiques. Processus stochastiques. Processus de diffusion. Équations différentielles non linéaires. TECHNOLOGY & ENGINEERING Engineering (General) bisacsh TECHNOLOGY & ENGINEERING Reference. bisacsh Differential equations, Nonlinear fast Diffusion processes fast Engineering Mathematical models fast Stochastic processes fast Willander, M. https://id.oclc.org/worldcat/entity/E39PCjyMkmcMTJRpKKtdGWp8wd http://id.loc.gov/authorities/names/n97015960 has work: High-dimensional nonlinear diffusion stochastic processes (Text) https://id.oclc.org/worldcat/entity/E39PCH6dmX34dPQRRkrJdyGKBd https://id.oclc.org/worldcat/ontology/hasWork Print version: Mamontov, Yevgeny, 1955- High-dimensional nonlinear diffusion stochastic processes. Singapore ; River Edge, NJ : World Scientific, 2001 9810243855 9789810243852 (DLC) 00053437 (OCoLC)45283225 Series on advances in mathematics for applied sciences ; v. 56. http://id.loc.gov/authorities/names/n90710999 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=235902 Volltext |
| spellingShingle | Mamontov, Yevgeny, 1955- High-dimensional nonlinear diffusion stochastic processes : modelling for engineering applications / Series on advances in mathematics for applied sciences ; Preface; Contents; Chapter 1 Introductory Chapter; 1.1 Prerequisites for Reading; 1.2 Random Variable. Stochastic Process. Random Field. High-Dimensional Process. One-Point Process; 1.3 Two-Point Process. Expectation. Markov Process. Example of Non-Markov Process Associated with Multidimensional Markov Process; 1.4 Preceding Subsequent and Transition Probability Densities. The Chapman-Kolmogorov Equation. Initial Condition for Markov Process; 1.4.1 The Chapman-Kolmogorov equation; 1.4.2 Initial condition for Markov process. 1.5 Homogeneous Markov Process. Example of Markov Process: The Wiener Process1.6 Expectation Variance and Standard Deviations of Markov Process; 1.7 Invariant and Stationary Markov Processes. Covariance. Spectral Densities; 1.8 Diffusion Process; 1.9 Example of Diffusion Processes: Solutions of Ito's Stochastic Ordinary Differential Equation; 1.10 The Kolmogorov Backward Equation; 1.11 Figures of Merit. Diffusion Modelling of High-Dimensional Systems; 1.12 Common Analytical Techniques to Determine Probability Densities of Diffusion Processes. The Kolmogorov Forward Equation. 1.12.1 Probability density1.12.2 Invariant probability density; 1.12.3 Stationary probability density; 1.13 The Purpose and Content of This Book; Chapter 2 Diffusion Processes; 2.1 Introduction; 2.2 Time-Derivatives of Expectation and Variance; 2.3 Ordinary Differential Equation Systems for Expectation; 2.3.1 The first-order system; 2.3.2 The second-order system; 2.3.3 Systems of the higher orders; 2.4 Models for Noise-Induced Phenomena in Expectation; 2.4.1 The case of stochastic resonance; 2.4.2 Practically efficient implementation of the second-order system. 2.5 Ordinary Differential Equation System for Variance2.5.1 Damping matrix; 2.5.2 The uncorrelated-matrixes approximation; 2.5.3 Nonlinearity of the drift function; 2.5.4 Fundamental limitation of the state-space-independent approximations for the diffusion and damping matrixes; 2.6 The Steady-State Approximation for The Probability Density; Chapter 3 Invariant Diffusion Processes; 3.1 Introduction; 3.2 Preliminary Remarks; 3.3 Expectation. The Finite-Equation Method; 3.4 Explicit Expression for Variance; 3.5 The Simplified Detailed-Balance Approximation for Invariant Probability Density. 3.5.1 Partial differential equation for logarithm of the density3.5.2 Truncated equation for the logarithm and the detailed-balance equation; 3.5.3 Case of the detailed balance; 3.5.4 The detailed-balance approximation; 3.5.5 The simplified detailed-balance approximation. Theorem on the approximating density; 3.6 Analytical-Numerical Approach to Non-Invariant and Invariant Diffusion Processes; 3.6.1 Choice of the bounded domain of the integration; 3.6.2 Evaluation of the multifold integrals. The Monte Carlo technique; 3.6.3 Summary of the approach; 3.7 Discussion. Engineering Mathematical models. Stochastic processes. http://id.loc.gov/authorities/subjects/sh85128181 Diffusion processes. http://id.loc.gov/authorities/subjects/sh85037941 Differential equations, Nonlinear. http://id.loc.gov/authorities/subjects/sh85037906 Stochastic Processes https://id.nlm.nih.gov/mesh/D013269 Ingénierie Modèles mathématiques. Processus stochastiques. Processus de diffusion. Équations différentielles non linéaires. TECHNOLOGY & ENGINEERING Engineering (General) bisacsh TECHNOLOGY & ENGINEERING Reference. bisacsh Differential equations, Nonlinear fast Diffusion processes fast Engineering Mathematical models fast Stochastic processes fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85128181 http://id.loc.gov/authorities/subjects/sh85037941 http://id.loc.gov/authorities/subjects/sh85037906 https://id.nlm.nih.gov/mesh/D013269 |
| title | High-dimensional nonlinear diffusion stochastic processes : modelling for engineering applications / |
| title_auth | High-dimensional nonlinear diffusion stochastic processes : modelling for engineering applications / |
| title_exact_search | High-dimensional nonlinear diffusion stochastic processes : modelling for engineering applications / |
| title_full | High-dimensional nonlinear diffusion stochastic processes : modelling for engineering applications / Yevgeny Mamontov, Magnus Willander. |
| title_fullStr | High-dimensional nonlinear diffusion stochastic processes : modelling for engineering applications / Yevgeny Mamontov, Magnus Willander. |
| title_full_unstemmed | High-dimensional nonlinear diffusion stochastic processes : modelling for engineering applications / Yevgeny Mamontov, Magnus Willander. |
| title_short | High-dimensional nonlinear diffusion stochastic processes : |
| title_sort | high dimensional nonlinear diffusion stochastic processes modelling for engineering applications |
| title_sub | modelling for engineering applications / |
| topic | Engineering Mathematical models. Stochastic processes. http://id.loc.gov/authorities/subjects/sh85128181 Diffusion processes. http://id.loc.gov/authorities/subjects/sh85037941 Differential equations, Nonlinear. http://id.loc.gov/authorities/subjects/sh85037906 Stochastic Processes https://id.nlm.nih.gov/mesh/D013269 Ingénierie Modèles mathématiques. Processus stochastiques. Processus de diffusion. Équations différentielles non linéaires. TECHNOLOGY & ENGINEERING Engineering (General) bisacsh TECHNOLOGY & ENGINEERING Reference. bisacsh Differential equations, Nonlinear fast Diffusion processes fast Engineering Mathematical models fast Stochastic processes fast |
| topic_facet | Engineering Mathematical models. Stochastic processes. Diffusion processes. Differential equations, Nonlinear. Stochastic Processes Ingénierie Modèles mathématiques. Processus stochastiques. Processus de diffusion. Équations différentielles non linéaires. TECHNOLOGY & ENGINEERING Engineering (General) TECHNOLOGY & ENGINEERING Reference. Differential equations, Nonlinear Diffusion processes Engineering Mathematical models Stochastic processes |
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| work_keys_str_mv | AT mamontovyevgeny highdimensionalnonlineardiffusionstochasticprocessesmodellingforengineeringapplications AT willanderm highdimensionalnonlineardiffusionstochasticprocessesmodellingforengineeringapplications |