Udemy - ML-Fluid Mechanics Integration for Thermal Flow Predicati...
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- Language English
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ML-Fluid Mechanics Integration for Thermal Flow Predication
https://WebToolTip.com
Published 1/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 4m | Size: 1.4 GB
Bridge data intelligence and physics
What you'll learn
Introduction to ML-CFD integration, including the motivations and applications in thermal flow prediction.
Fundamentals of fluid mechanics relevant to ML models: Navier-Stokes equations, conservation laws, buoyancy, turbulence, and dimensional analysis.
Machine learning approaches in physical systems, including neural architectures, physics-informed models, reduced-order modeling, and case studies.
Synthetic data generation for ML-CFD: dataset design, voxelization, data augmentation, and physical consistency verification.
Training convolutional neural networks (CNNs) for CFD prediction including architectures, loss functions, hyperparameter tuning, and overfitting avoidance.
Physics-informed neural networks (PINNs) applied to fluid mechanics problems, challenges, and scaling strategies.
Uncertainty quantification methods for reliability assessment and extrapolation handling.
Validation of ML models against high-fidelity CFD simulations using error metrics and visualization.
Integration of hybrid ML-CFD methods into real-time design and optimization workflows.
Comparative analysis of hybrid ML-CFD and classical CFD approaches in terms of speed, accuracy, hardware needs, and industry implications.
Advanced topics such as turbulent flow prediction with ML methods and dataset enhancement for multi-physics correlational analysis.
Future prospects and practical adoption in engineering research and development.
Requirements
There are no strict prerequisites for this course, making it accessible to beginners interested in machine learning and computational fluid dynamics (CFD). The course is designed to guide learners from foundational concepts to advanced applications, ensuring that even those without prior expertise can follow along. Foundational Knowledge • Basic understanding of physics and mathematics, particularly calculus and differential equations, will be helpful but is not required, as key concepts like the Navier-Stokes equations and conservation laws are introduced within the course. • Familiarity with engineering principles such as thermal flows, boundary conditions, and dimensional analysis is beneficial but not mandatory, as these are covered in the fundamentals section. Technical Skills • No prior experience in machine learning or CFD is required. The course includes introductory modules on neural network architectures, physics-informed models, and reduced-order modeling. • Programming skills are not explicitly required, though exposure to Python or scientific computing may enhance the learning experience when implementing models. Tools and Equipment • Access to a standard computer is sufficient for understanding the course content. While advanced applications may involve CNNs and PINNs, the course does not require specialized hardware like GPUs for learning purposes. • All necessary tools and workflows, including synthetic data generation and model validation, are explained step by step, minimizing the need for external software or prior technical setup. This course lowers barriers for beginners by integrating theoretical and practical components in a structured, self-contained format, enabling learners from diverse backgrounds to engage with hybrid ML-CFD methodologies.
Files:
[ WebToolTip.com ] Udemy - ML-Fluid Mechanics Integration for Thermal Flow Predication- Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 1 - Introduction to ML CFD Integration
- 1. Overview of computational fluid dynamics (CFD) (Description).html (1.8 KB)
- 1. Overview of computational fluid dynamics (CFD).mp4 (67.8 MB)
- 2. Limitations of traditional CFD methods (Description).html (1.1 KB)
- 2. Limitations of traditional CFD methods.mp4 (32.4 MB)
- 3. Emergence of machine learning in fluid mechanics (Description).html (1.2 KB)
- 3. Emergence of machine learning in fluid mechanics.mp4 (49.0 MB)
- 4. Hybrid modelling motivation (Description).html (1.1 KB)
- 4. Hybrid modelling motivation.mp4 (43.4 MB)
- 5. Scope and applications in thermal flow prediction (Description).html (1.2 KB)
- 5. Scope and applications in thermal flow prediction.mp4 (57.2 MB)
- 10. Boundary conditions and Richardson number significance (Description).html (3.6 KB)
- 10. Boundary conditions and Richardson number significance.mp4 (39.9 MB)
- 11. Dimensional analysis in CFD (Description).html (1.3 KB)
- 11. Dimensional analysis in CFD.mp4 (61.0 MB)
- 6. Navier Stokes equations (Description).html (1.3 KB)
- 6. Navier Stokes equations.mp4 (44.5 MB)
- 7. 202 Conservation laws mass momentum energy.docx (16.5 KB)
- 7. Conservation laws mass momentum energy (Description).html (1.0 KB)
- 7. Conservation laws mass momentum energy.mp4 (24.5 MB)
- 8. Buoyancy effects and the Boussinesq approximation (Description).html (1.3 KB)
- 8. Buoyancy effects and the Boussinesq approximation.mp4 (32.5 MB)
- 9. 204 Laminar vs Turbulent regimes.docx (18.5 KB)
- 9. Laminar vs Turbulent regimes (Description).html (0.8 KB)
- 9. Laminar vs Turbulent regimes.mp4 (68.9 MB)
- 12. Data driven vs Physics informed approches (Description).html (1.3 KB)
- 12. Data driven vs Physics informed approches.mp4 (55.5 MB)
- 13. Neural network architectures for physical systems (Description).html (2.3 KB)
- 13. Neural network architectures for physical systems.mp4 (41.2 MB)
- 14. Proper orthogonal decomposition (POD) and reduced order models (ROMs) (Description).html (1.3 KB)
- 14. Proper orthogonal decomposition (POD) and reduced order models (ROMs).mp4 (46.5 MB)
- 15. Embedding physical constraints in ML models (Description).html (1.3 KB)
- 15. Embedding physical constraints in ML models.mp4 (72.7 MB)
- 16. Case studies turbulence modelling heat transfer prediction (Description).html (1.3 KB)
- 16. Case studies turbulence modelling heat transfer prediction.mp4 (49.5 MB)
- 17. Reduced physics CFD simulations (Description).html (1.6 KB)
- 17. Reduced physics CFD simulations.mp4 (53.4 MB)
- 18. Dataset design geometries flow regimes boundary diversity (Description).html (1.0 KB)
- 18. Dataset design geometries flow regimes boundary diversity.mp4 (65.2 MB)
- 19. Voxelization of geometric inputs (256³ resolution) (Description).html (1.6 KB)
- 19. Voxelization of geometric inputs (256³ resolution).mp4 (60.7 MB)
- 20. Ensuring physical consistency of synthetic data (Description).html (1.4 KB)
- 20. Ensuring physical consistency of synthetic data.mp4 (58.5 MB)
- 21. Data augmention strategies and bias mitigation (Description).html (1.3 KB)
- 21. Data augmention strategies and bias mitigation.mp4 (59.8 MB)
- 22. Statical verification of dataset (Description).html (1.3 KB)
- 22. Statical verification of dataset.mp4 (59.9 MB)
- 23. CNN encoder decoder architectures (Description).html (1.4 KB)
- 23. CNN encoder decoder architectures.mp4 (33.3 MB)
- 24. Convolution in volumetric data (Description).html (1.3 KB)
- 24. Convolution in volumetric data.mp4 (51.1 MB)
- 25. Loss functions incorporating physical constraints (Description).html (1.1 KB)
- 25. Loss functions incorporating physical constraints.mp4 (52.2 MB)
- 26. 504 Training workflows.docx (17.4 KB)
- 26. Training workflow (Description).html (0.8 KB)
- 26. Training workflow.mp4 (23.7 MB)
- 27. Hyperparameter optimization (Description).html (2.9 KB)
- 27. Hyperparameter optimization.mp4 (23.2 MB)
- 28. Avoiding overfitting and promoting generalization (Description).html (0.9 KB)
- 28. Avoiding overfitting and promoting generalization.mp4 (44.6 MB)
- 29. Implementation best practices (Description).html (1.2 KB)
- 29. Implementation best practices.mp4 (57.2 MB)
- Bonus Resources.txt (0.1 KB)
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