Innovative Integration of Machine Learning and Fluid Mechanics for Enhanced Thermal Flow Prediction.

1. Gain the ability to articulate the fundamentals of CFD, recognize its limitations, and explain how machine learning is revolutionizing fluid mechanics for thermal flow prediction applications.​
2. Apply core principles of fluid mechanics, such as the Navier–Stokes equations and conservation laws, to machine learning contexts and analyze flow regimes and boundary conditions.​
3. Distinguish between data-driven and physics-informed machine learning approaches, describe neural network architectures, and understand model reduction for physical systems.​
4. Design synthetic datasets for ML-CFD, perform voxelization of geometric data, ensure physical consistency, and apply data augmentation and statistical verification methods.​
5. Construct and train convolutional neural network encoder–decoder models for CFD, optimize hyperparameters, and implement best practices to avoid overfitting and improve generalization.​
6. Formulate and apply physics-informed neural networks to fluid mechanics problems, solve forward and inverse tasks, and compare PINNs with CNN-based prediction methods.​
7. Implement uncertainty quantification using Monte Carlo dropout, assess prediction reliability, and integrate uncertainty into engineering workflows for robust decision-making.​
8. Validate machine learning models against high-fidelity CFD using error metrics, visualization, and generalization testing, and compare results with classical CFD approaches.​
9. Integrate ML-CFD into real-time design pipelines, conduct parametric and gradient-based optimization, and leverage computational gains for efficient engineering workflows.​
10. Analyze and compare hybrid CNN-CFD and classical CFD approaches, evaluating speed, accuracy, scalability, and practical considerations for industry adoption.​
11. Address turbulence modeling challenges, utilize neural architectures for multiscale flows, and explore future research directions and case studies in ML-CFD.​
12. Enhance datasets, validate energy conservation, analyze multivariate correlations, and improve interpretability across thermal, seismic, and heat transfer domains.​
13. Summarize the course methodology, identify future challenges, discuss multi-physics simulation expansion, and evaluate real-world impact and adoption potential.

Training Venue

Virtual

Class Outline (Table of Content)

• Day 1 - Thursday Mar 19, 2026

  09:00 AM to 04:00 PM

  Budapest

  ( 7 hours 0 minutes )
• ML-Fluid Mechanics Integration for Thermal Flow Predication
  • Practical assignments, coding, and ML-CFD applications. By the end of this section, learners will understand the fundamentals of CFD, recognize its limitations, and grasp how machine learning enhances fluid and thermal flow predictions. They will be able to apply core fluid mechanics principles, distinguish between data-driven and physics-informed ML approaches, design datasets, implement neural network models, and evaluate predictions for real-world engineering applications.

Cost Per Attendee (USD)

$200

Who Should Attend This Online Class?

• Engineers and researchers in fluid mechanics or thermal sciences who want to integrate machine learning with traditional CFD techniques.

• Data scientists and machine learning practitioners interested in applying ML to physical systems, particularly fluid flows and heat transfer problems.

• Graduate students and academics in mechanical, aerospace, chemical, or civil engineering looking to expand their knowledge of ML-CFD applications.

• Simulation and CFD professionals aiming to accelerate design workflows, optimize systems, and improve predictive capabilities with data-driven methods.

• Industry professionals in energy, aerospace, automotive, or process engineering seeking to leverage ML for real-time simulation, uncertainty quantification, and design optimization.

• Researchers exploring hybrid modeling approaches—combining physics-based simulations with neural networks for enhanced speed, accuracy, and scalability.

• Anyone interested in advanced modeling techniques for turbulence, multiscale flows, and thermal-fluid systems, including physics-informed machine learning applications.

Why Should You Attend This Online Class?

Class Preparation

• Know the basics of fluid mechanics (Navier–Stokes equations, flow, and heat transfer).

• Understand CFD concepts and turbulence modeling.

• Have some machine learning knowledge (neural networks, supervised learning).

• Be comfortable with Python programming and libraries like NumPy or PyTorch.

• Have access to a computer with enough RAM/GPU for training models.

• Be ready to code, work with data, and visualize results.

Instructor Details