- Išsiųsime per 10–14 d.d.
/images/books/12975985/9783030447205.jpg)
- Leidėjas: Springer
- ISBN-10: 3030447200
- ISBN-13: 9783030447205
- Formatas: 15.6 x 23.4 x 1.6 cm, minkšti viršeliai
- Kalba: Anglų
- Extra -15 % nuolaida šiai knygai su kodu: ENG15
Data Analysis for Direct Numerical Simulations of Turbulent Combustion + nemokamas atvežimas! | knygos.lt
Atsiliepimai
Aprašymas
This book presents methodologies for analysing large data sets produced by the direct numerical simulation (DNS) of turbulence and combustion. It describes the development of models that can be used to analyse large eddy simulations, and highlights both the most common techniques and newly emerging ones.
The chapters, written by internationally respected experts, invite readers to consider DNS of turbulence and combustion from a formal, data-driven standpoint, rather than one led by experience and intuition. This perspective allows readers to recognise the shortcomings of existing models, with the ultimate goal of quantifying and reducing model-based uncertainty. In addition, recent advances in machine learning and statistical inferences offer new insights on the interpretation of DNS data.
EXTRA 15 % nuolaida su kodu: ENG15
Akcija baigiasi už 5d.11:25:00
Nuolaidos kodas galioja perkant nuo 10 €. Nuolaidos nesumuojamos.
- Leidėjas: Springer
- ISBN-10: 3030447200
- ISBN-13: 9783030447205
- Formatas: 15.6 x 23.4 x 1.6 cm, minkšti viršeliai
- Kalba: Anglų
This book presents methodologies for analysing large data sets produced by the direct numerical simulation (DNS) of turbulence and combustion. It describes the development of models that can be used to analyse large eddy simulations, and highlights both the most common techniques and newly emerging ones.
The chapters, written by internationally respected experts, invite readers to consider DNS of turbulence and combustion from a formal, data-driven standpoint, rather than one led by experience and intuition. This perspective allows readers to recognise the shortcomings of existing models, with the ultimate goal of quantifying and reducing model-based uncertainty. In addition, recent advances in machine learning and statistical inferences offer new insights on the interpretation of DNS data.
Atsiliepimai