Monografie
Improving the analysis of aeroacoustic measurements through machine learning
- Sprache
-
Englisch
- Identifier
-
1314379518
- Thema
-
Strömungsakustik; Schallabstrahlung; Numerische Strömungssimulation; Schallgeber; Schallausbreitung
- Beteiligte Personen und Organisationen
-
Goudarzi, Armin
Spehr, Carsten
Herbold, Steffen
Ecker, Alexander
- URN
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
09.01.2024, 11:56 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Monografie
Beteiligte
- Goudarzi, Armin
- Spehr, Carsten
- Herbold, Steffen
- Ecker, Alexander
Ähnliche Objekte (12)
Improving Predictive Modeling With Machine Learning Techniques
Improving CAT bond pricing models via machine learning
Improving and interpreting machine learning algorithms with applications
Robustifying machine learning through weakening supervision
Improving drill-core hyperspectral mineral mapping using machine learning
Prediction of Protein Function through Machine Learning
Global optimization of processes through machine learning
Mesozooplankton studies through automation and machine learning
Improving Scoring Functions for Protein Docking by Machine Learning and Learning Data
Improving Google’s open-source machine learning system TensorFlow
Application of machine learning in beam optics measurements and corrections
Understanding Toll-like Receptor Modulation Through Machine Learning
Improving Predictive Modeling With Machine Learning Techniques
Improving CAT bond pricing models via machine learning
Improving and interpreting machine learning algorithms with applications
Robustifying machine learning through weakening supervision
Improving drill-core hyperspectral mineral mapping using machine learning
Prediction of Protein Function through Machine Learning
Global optimization of processes through machine learning
Mesozooplankton studies through automation and machine learning
Improving Scoring Functions for Protein Docking by Machine Learning and Learning Data
Improving Google’s open-source machine learning system TensorFlow
Application of machine learning in beam optics measurements and corrections
Understanding Toll-like Receptor Modulation Through Machine Learning
Improving Predictive Modeling With Machine Learning Techniques
Improving CAT bond pricing models via machine learning
Improving and interpreting machine learning algorithms with applications
Robustifying machine learning through weakening supervision
Improving drill-core hyperspectral mineral mapping using machine learning
Prediction of Protein Function through Machine Learning
Global optimization of processes through machine learning
Mesozooplankton studies through automation and machine learning
Improving Scoring Functions for Protein Docking by Machine Learning and Learning Data
Improving Google’s open-source machine learning system TensorFlow
Application of machine learning in beam optics measurements and corrections