Machine Learning for Engineering & Science Applications | IIT Madras - #102 Application | Part 2 | Solution | Machine Learning for Engineering & Science Applications

Welcome to 'Machine Learning for Engineering & Science Applications' course ! This lecture reveals a cutting-edge solution to the CFD surrogate modeling problem using Convolutional Neural Networks (CNNs). We'll delve into the work of researchers at Autodesk, who pioneered the use of CNNs for rapid flow field prediction. The lecture will explain their clever approach of using an encoder-decoder structure, where the encoder extracts features from an image representation of the flow and the decoder reconstructs the predicted velocity field. We'll discuss the choice of input, output, and loss function, highlighting the effectiveness of this method in capturing the complex patterns of fluid flow. Prepare to be amazed by the potential of deep learning in revolutionizing CFD! 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 #CFD #CNN #SurrogateModel #Autodesk #EncoderDecoderStructure #LossFunction #Input #Output #Architecture