
EAN: 9783031162503

Bilder-Quelle: discount24.de - Sport-Freizeit
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.
Produktinformationen zuletzt aktualisiert am
16.08.2025 um 00:35 Uhr
16.08.2025 um 00:35 Uhr
Hersteller
-
EAN
9783031162503
MPN
-
ASIN
3031162501
Produktgruppe
-

Produktzustand:
Verfügbarkeit:
Versandkosten:
Sonderpreis:

Sie sind Shopbetreiber? Listen Sie ganz einfach Ihre Produkte hier bei uns im Portal >>>
Letzte EAN Aktualisierungen:
9781491937990 - Adrian Kaehler - GEBRAUCHT Learning OpenCV 3 Compu...9781484228227 - Python Machine Learning Case Studies - Danish Haro...
9783319746890 - The International Conference on Advanced Machine L...
9783642354274 - Machine Learning in Medical Imaging Kartoniert (TB...
9783319142302 - SpringerBriefs in Computer Science Automatic Des...
9781484277799 - Practical AI for Healthcare Professionals - Abhina...
9783035625851 - Co-Corporeality of Humans Machines & Microbes Kart...
9783642373428 - Knowledge Engineering Machine Learning and Lattice...
9789811578762 - Statistical Learning with Math and Python - Joe Su...
9783319504773 - Machine Learning for Health Informatics Kartoniert...
9781071614174 - An Introduction to Statistical Learning - Gareth J...
9783319965611 - Machine Learning for Dynamic Software Analysis Pot...
kürzlich hinzugefügt:
9789811967023 - SpringerBriefs in Computer Science Latent Factor...9783319409993 - Advances in Swarm Intelligence Kartoniert (TB)...
9783319219028 - Undergraduate Topics in Computer Science Introdu...
9783319668079 - Machine Learning and Knowledge Extraction Kartonie...
9789813366930 - Cybernetics Cognition and Machine Learning Applica...
9789811301995 - Evolutionary Approach to Machine Learning and Deep...
9781484289778 - Time Series Algorithms Recipes - Akshay R Kulkarni...
9781484265451 - Deploy Machine Learning Models to Production - Pra...
9783319633114 - Intelligent Computing Theories and Application Kar...
9783031040825 - xxAI - Beyond Explainable AI Kartoniert (TB)...
9781484264201 - Develop Intelligent iOS Apps with Swift - Özgür ...
9789811066979 - SpringerBriefs in Applied Sciences and Technology ...