Parente alessandro (57 Ergebnisse)
Sprache: Englisch
Verlag: Springer, 2023
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Sprache: Englisch
Verlag: Springer International Publishing AG, CH, 2023
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Paperback. Zustand: New. 1st ed. 2023. This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows.These two fields, ML and turbulent combustion, have…large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world's total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and "greener" combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation.
Sprache: Englisch
Verlag: Springer, 2023
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Sprache: Englisch
Verlag: Springer, 2023
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Sprache: Englisch
Verlag: Springer, 2023
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Sprache: Englisch
Verlag: Springer, 2023
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Sprache: Englisch
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Sprache: Englisch
Verlag: Springer, 2023
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Sprache: Englisch
Verlag: Springer, 2023
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Zustand: New. 1st ed. 2023 edition NO-PA16APR2015-KAP.
Sprache: Englisch
Verlag: Springer, 2023
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Sprache: Englisch
Verlag: Springer 2023-01, 2023
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PF. Zustand: New.
Sprache: Englisch
Verlag: Springer International Publishing AG, Cham, 2023
- Hardcover
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Hardcover. Zustand: new. Hardcover. This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows.These two fields, ML and turbulent combustion, have lar…ge body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the worlds total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and greener combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Paperback. Zustand: New.
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Paperback. Zustand: New.
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Sprache: Englisch
Verlag: Springer, 2023
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Machine Learning and Its Application to Reacting Flows: Ml and Combustion
Swaminathan, Nedunchezhian (Editor)/ Parente, Alessandro (Editor)
Sprache: Englisch
Verlag: Springer Nature, 2022
- Softcover
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Paperback. Zustand: Brand New. 357 pages. 9.25x6.10x0.75 inches. In Stock.
Sprache: Englisch
Verlag: SPRINGER NP, 2023
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Verlag: Our Lady of Grace Capuchin Friary, 2004
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In den WarenkorbPaperback. Zustand: Good+. Light wear to wraps with light rubbing light creasing; No creasing to spine, very clean, no markings g+; 366 pages.
Sprache: Englisch
Verlag: SPRINGER NP, 2023
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Machine Learning and Its Application to Reacting Flows: ML and Combustion
Swaminathan, Nedunchezhian (Edited by)/ Parente, Alessandro (Edited by)
Sprache: Englisch
Verlag: Springer, 2022
- Hardcover
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Hardcover. Zustand: Brand New. 357 pages. 9.25x6.10x0.94 inches. In Stock.
Sprache: Englisch
Verlag: Springer International Publishing AG, CH, 2023
- Softcover
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Paperback. Zustand: New. 1st ed. 2023. This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows.These two fields, ML and turbulent combustion, have…large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world's total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and "greener" combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation.
Weitere BilderSprache: Englisch
Verlag: Springer, 2023
- Softcover
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Taschenbuch. Zustand: Neu. Machine Learning and Its Application to Reacting Flows | ML and Combustion | Nedunchezhian Swaminathan (u. a.) | Taschenbuch | Lecture Notes in Energy | xi | Englisch | 2023 | Springer | EAN 9783031162503 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, ju…ergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer, 2023
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows.These two f…ields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world's total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and 'greener' combustion systems that are friendlier to the environment can be designed.The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation.
Sprache: Englisch
Verlag: Springer, 2023
- Hardcover
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows.These two fields,…ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world's total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and 'greener' combustion systems that are friendlier to the environment can be designed.The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation.
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Zustand: fine. très bon état. Optez pour nos livres d'occasion en très bon état, et soutenez l'insertion sociale et l'écologie en leur offrant une seconde vie,Sous Blister. 322097-1 - Lille à voir à vivre, Delevoye, Philippe, 2020.
Sprache: Englisch
Verlag: Springer, 2023
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Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 360 | Sprache: Englisch | Produktart: Bücher | This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbu…lent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world¿s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and ¿greener¿ combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation.
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