Machine Learning for Engineering & Science Applications | IIT Madras - #99 Application | Part 1 | Description | Fin Heat Transfer

Unlock the Future: Master AI & Machine Learning with NPTEL-IITM’s Comprehensive Course! Dive into Neural Networks, Deep Learning, Probabilities, and Optimization Techniques tailored for Engineering & Science Applications. Your AI journey starts here!

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What you'll learn

Understand the historical development and foundational concepts of artificial intelligence.
Gain proficiency in applying machine learning techniques to engineering and science problems.
Develop skills in using linear algebra and calculus for machine learning modeling.
Learn to implement and optimize machine learning algorithms using Python packages.

This course includes

32 hours of video
Certificate of completion
Access on mobile and TV

Summary

Keywords

Application 1 description - Fin Heat Transfer

Full Transcript

Welcome to 'Machine Learning for Engineering & Science Applications' course ! In this lecture, we'll dive into a practical engineering problem: predicting heat transfer in fins. Fins are commonly used to enhance heat dissipation from surfaces, and understanding their behavior is crucial in various engineering applications. We'll explore how to build a surrogate model using deep learning to predict the temperature distribution along a fin, given parameters like base temperature, fin geometry, and environmental conditions. You'll see how a dataset containing measurements of these parameters and the corresponding temperatures can be used to train a neural network that accurately captures the complex heat transfer phenomenon. NPTEL Courses permit certifications that can be used for Course Credits in Indian Universities as per the UGC and AICTE notifications. To understand various certification options for this course, please visit https://nptel.ac.in/courses/106106198 #FinHeatTransfer #SurrogateModel #Data #TemperaturePrediction #FunctionApproximation