Bridge data intelligence and physics
This course contains the use of artificial intelligence
ML-Fluid Mechanics Integration for Thermal Flow Predication course will learn to integrate machine learning with computational fluid dynamics (CFD) for advanced thermal flow prediction and engineering design optimization. They will cover fundamentals of fluid mechanics, machine learning architectures for physics-based systems, synthetic data generation, physics-informed neural networks, uncertainty quantification, model validation, and real-time design process integration.
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..
Course Content
- Introduction to ML-CFD Integration –> 5 lectures • 22min.
- Fundamentals of Fluid Mechanics for ML Models –> 6 lectures • 25min.
- Overview of Machine Learning in Physics – Based Systems –> 5 lectures • 26min.
- Synthetic Data Generation for ML-CFD –> 6 lectures • 28min.
- Convolutional Neural Networks for CFD Prediction –> 7 lectures • 23min.
Requirements
This course contains the use of artificial intelligence
ML-Fluid Mechanics Integration for Thermal Flow Predication course will learn to integrate machine learning with computational fluid dynamics (CFD) for advanced thermal flow prediction and engineering design optimization. They will cover fundamentals of fluid mechanics, machine learning architectures for physics-based systems, synthetic data generation, physics-informed neural networks, uncertainty quantification, model validation, and real-time design process integration.
Key Learning Areas
- 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 modelling, 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.
The course is structured with about 29 lectures totalling around 6 hours, covering both theoretical foundations and practical applications in ML-accelerated CFD design.
Also, please make sure the Udemy course includes a note disclosing the use of Artificial Intelligence in the course description, as required by their guidelines.