Machine Learning for CFD Applications in Chemical Engineering
Machine Learning for CFD Applications in Chemical Engineering
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Empowering Engineers to Predict, Optimize, and Innovate
About Training
About Training
The fusion of Machine Learning (ML) with Computational Fluid Dynamics (CFD) is reshaping chemical engineering by enabling faster simulations, digital twins, and predictive modeling for complex industrial processes. This 3-week intensive training program empowers engineers, researchers, and professionals to integrate ML techniques such as PCA, Autoencoders (AE), POD-ROM, and AT-ROM into real-world CFD workflows.
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Registration
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Course Details
Course Details
Duration: 3 Weeks | 2 Hours per Day | 15 Sessions
Topics Covered:
Preprocess and analyze CFD simulation data for ML pipelines.
Apply PCA and Autoencoders for dimensionality reduction and surrogate modeling.
Build POD-ROM and AT-ROM models for accurate prediction of unseen scenarios.
Integrate ML surrogates into digital twin frameworks for real-time decision-making.
Visualize and validate ML predictions against CFD simulation results.
Who Should Join:
CFD Engineers & Chemical Engineers
Process Design Engineers & Plant Professionals
Graduate Students & Researchers
Industry Experts exploring Digital Twin solutions
Softwares:
ANSYS Fluent, Python (NumPy, Scikit-learn, TensorFlow), ParaView
Training catalogue
Training catalogue
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