Data Driven Analysis and Modeling of Turbulent Flows
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Data Driven Analysis and Modeling of Turbulent Flows
Duraisamy, Karthik
Elsevier Science & Technology
03/2025
460
Mole
9780323950435
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1. A roadmap for data-driven analysis and modeling of turbulent flows
2. Modal decomposition: POD, SPOD, DMD
3. Statistical learning: Neural nets, sparse regression, Lasso
4. Resolvents
5. Projection-based Reduced Order Modeling
6. Data-assimilation and flow estimation
7. Data-driven control
8. Model-consistent inference and learning
9. Parameter estimation and uncertainty quantification
10. Stress representations
11. Evolutionary optimization
12. Emerging topics: Super resolution
2. Modal decomposition: POD, SPOD, DMD
3. Statistical learning: Neural nets, sparse regression, Lasso
4. Resolvents
5. Projection-based Reduced Order Modeling
6. Data-assimilation and flow estimation
7. Data-driven control
8. Model-consistent inference and learning
9. Parameter estimation and uncertainty quantification
10. Stress representations
11. Evolutionary optimization
12. Emerging topics: Super resolution
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Bayesian statistics; Constitutive model; data-assimilation; data-driven turbulence modeling; dimensionality reduction; dynamic mode decomposition; dynamic observer; estimation; explicit algebraic stress model EASM; feature engineering; Flow control; gene expression programming; impulse response models; inverse modeling; large-eddy simulation; learning from data; linear stability; low-order modeling; machine learning; Modal decomposition; model-predictive control; parameter calibration; proper orthogonal decomposition; realizability triangle; resolvent analysis; Reynolds stress; Reynolds-averaged Navier-Stokes; scalar flux; sparse Bayesian learning; sparse identification; spectral analysis; subspace system identification; Symbolic regression algorithms; system identification; tensor basis; turbulence; turbulence model; Turbulence modeling; Turbulent flows; uncertainty quantification; wall turbulence;
1. A roadmap for data-driven analysis and modeling of turbulent flows
2. Modal decomposition: POD, SPOD, DMD
3. Statistical learning: Neural nets, sparse regression, Lasso
4. Resolvents
5. Projection-based Reduced Order Modeling
6. Data-assimilation and flow estimation
7. Data-driven control
8. Model-consistent inference and learning
9. Parameter estimation and uncertainty quantification
10. Stress representations
11. Evolutionary optimization
12. Emerging topics: Super resolution
2. Modal decomposition: POD, SPOD, DMD
3. Statistical learning: Neural nets, sparse regression, Lasso
4. Resolvents
5. Projection-based Reduced Order Modeling
6. Data-assimilation and flow estimation
7. Data-driven control
8. Model-consistent inference and learning
9. Parameter estimation and uncertainty quantification
10. Stress representations
11. Evolutionary optimization
12. Emerging topics: Super resolution
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Bayesian statistics; Constitutive model; data-assimilation; data-driven turbulence modeling; dimensionality reduction; dynamic mode decomposition; dynamic observer; estimation; explicit algebraic stress model EASM; feature engineering; Flow control; gene expression programming; impulse response models; inverse modeling; large-eddy simulation; learning from data; linear stability; low-order modeling; machine learning; Modal decomposition; model-predictive control; parameter calibration; proper orthogonal decomposition; realizability triangle; resolvent analysis; Reynolds stress; Reynolds-averaged Navier-Stokes; scalar flux; sparse Bayesian learning; sparse identification; spectral analysis; subspace system identification; Symbolic regression algorithms; system identification; tensor basis; turbulence; turbulence model; Turbulence modeling; Turbulent flows; uncertainty quantification; wall turbulence;
