Data-driven computational methods: parameter and operator estimations
Modern scientific computational methods are undergoing a transformative change; big data and statistical learning methods now have the potential to outperform the classical first-principles modeling paradigm. This book bridges this transition, connecting the theory of probability, stochastic process...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2018
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| Schlagwörter: | |
| Zusammenfassung: | Modern scientific computational methods are undergoing a transformative change; big data and statistical learning methods now have the potential to outperform the classical first-principles modeling paradigm. This book bridges this transition, connecting the theory of probability, stochastic processes, functional analysis, numerical analysis, and differential geometry. It describes two classes of computational methods to leverage data for modeling dynamical systems. The first is concerned with data fitting algorithms to estimate parameters in parametric models that are postulated on the basis of physical or dynamical laws. The second is on operator estimation, which uses the data to nonparametrically approximate the operator generated by the transition function of the underlying dynamical systems. This self-contained book is suitable for graduate studies in applied mathematics, statistics, and engineering. Carefully chosen elementary examples with supplementary MATLAB codes and appendices covering the relevant prerequisite materials are provided, making it suitable for self-study |
| Umfang: | xi, 158 pages |
| ISBN: | 9781108472470 |
Internformat
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| 338 | |b nc |2 rdacarrier | ||
| 520 | |a Modern scientific computational methods are undergoing a transformative change; big data and statistical learning methods now have the potential to outperform the classical first-principles modeling paradigm. This book bridges this transition, connecting the theory of probability, stochastic processes, functional analysis, numerical analysis, and differential geometry. It describes two classes of computational methods to leverage data for modeling dynamical systems. The first is concerned with data fitting algorithms to estimate parameters in parametric models that are postulated on the basis of physical or dynamical laws. The second is on operator estimation, which uses the data to nonparametrically approximate the operator generated by the transition function of the underlying dynamical systems. This self-contained book is suitable for graduate studies in applied mathematics, statistics, and engineering. Carefully chosen elementary examples with supplementary MATLAB codes and appendices covering the relevant prerequisite materials are provided, making it suitable for self-study | ||
| 650 | 4 | |a Probabilities | |
| 650 | 4 | |a Mathematical statistics | |
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Datensatz im Suchindex
| DE-BY-TUM_call_number | 0202 MAT 344f 2019 B 941 |
|---|---|
| DE-BY-TUM_katkey | 2408259 |
| DE-BY-TUM_location | 02 |
| DE-BY-TUM_media_number | 040008536012 |
| _version_ | 1821933737254846466 |
| any_adam_object | |
| author | Harlim, John |
| author_GND | (DE-588)1022751417 |
| author_facet | Harlim, John |
| author_role | aut |
| author_sort | Harlim, John |
| author_variant | j h jh |
| building | Verbundindex |
| bvnumber | BV045872649 |
| classification_tum | MAT 344f |
| ctrlnum | (OCoLC)1057461766 (DE-599)BVBBV045872649 |
| dewey-full | 519.2 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.2 |
| dewey-search | 519.2 |
| dewey-sort | 3519.2 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Book |
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| id | DE-604.BV045872649 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T18:37:12Z |
| institution | BVB |
| isbn | 9781108472470 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031255956 |
| oclc_num | 1057461766 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM |
| owner_facet | DE-91G DE-BY-TUM |
| physical | xi, 158 pages |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Cambridge University Press |
| record_format | marc |
| spellingShingle | Harlim, John Data-driven computational methods parameter and operator estimations Probabilities Mathematical statistics Dynamisches System (DE-588)4013396-5 gnd Stochastisches Modell (DE-588)4057633-4 gnd |
| subject_GND | (DE-588)4013396-5 (DE-588)4057633-4 |
| title | Data-driven computational methods parameter and operator estimations |
| title_auth | Data-driven computational methods parameter and operator estimations |
| title_exact_search | Data-driven computational methods parameter and operator estimations |
| title_full | Data-driven computational methods parameter and operator estimations John Harlim |
| title_fullStr | Data-driven computational methods parameter and operator estimations John Harlim |
| title_full_unstemmed | Data-driven computational methods parameter and operator estimations John Harlim |
| title_short | Data-driven computational methods |
| title_sort | data driven computational methods parameter and operator estimations |
| title_sub | parameter and operator estimations |
| topic | Probabilities Mathematical statistics Dynamisches System (DE-588)4013396-5 gnd Stochastisches Modell (DE-588)4057633-4 gnd |
| topic_facet | Probabilities Mathematical statistics Dynamisches System Stochastisches Modell |
| work_keys_str_mv | AT harlimjohn datadrivencomputationalmethodsparameterandoperatorestimations |
Paper/Kapitel scannen lassen
Teilbibliothek Physik
| Signatur: |
0202 MAT 344f 2019 B 941 Lageplan |
|---|---|
| Exemplar 1 | Ausleihbar Am Standort |