Machine Learning for Engineering & Science Applications | IIT Madras - #105 Application | Part 4 | Solution of PDE/ODE using Neural Networks

Welcome to 'Machine Learning for Engineering & Science Applications' course ! Prepare to be mind-blown as we delve into a novel and groundbreaking application of neural networks: solving differential equations! Differential equations are the language of physics and engineering, describing a wide range of phenomena, but solving them analytically can be challenging. This lecture introduces the revolutionary idea of using neural networks to approximate solutions to PDEs and ODEs. We'll see how the backpropagation algorithm, typically used for training neural networks, can be repurposed to compute derivatives required for solving these equations. Using automatic differentiation in Tensorflow, we'll define a loss function based on the residual of the differential equation and train our network to find an accurate solution. 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 #SolutionOfPDEODE #NeuralNetworks #DifferentialEquations #Backpropagation #AutomaticDifferentiation #LossFunction #CollocationPoints #Tensorflow