ABOUT THE COURSE: The principal objective of the course is to give the students an overview of inverse problems in heat transfer and ways of formulating and solving them through examples. A wide range of inverse techniques including classical techniques, probabilistic techniques as well as modern techniques involving Machine Learning will be covered.INTENDED AUDIENCE: UG/PGPREREQUISITES: Undergraduate course in Heat Transfer would be useful. Knowledge of probability, calculus and matrix algebra.INDUSTRY SUPPORT: Thermal Engineers in the industry. Professionals interested in inverse methods (including in medical imaging)
Inverse Methods in Heat Transfer
Indian Institute of Technology Madras and NPTEL via Swayam
This course may be unavailable.
Overview
Syllabus
Week 1:Introduction to inverse problemsWeek 2:Statistical description of errors, Inverse problems as optimization problemsWeek 3:Classical Techniques, Calculation of sensitivity coefficientsWeek 4:Parameter and Function estimation. Introduction to Nonlinear TechniquesWeek 5:The Levenberg-Marquardt method, Tikhonov regularizationWeek 6:Probability Theory, Bayesian Framework, Week 7:Markov Chain Monte Carlo Methods (MCMC)Week 8:Metropolis-Hastings algorithm (MH), Computational ImplementationWeek 9:Introduction to Machine LearningWeek 10:Deep Learning – ANNs, CNNs, RNNsWeek 11:Surrogate Models for Inverse problems, Genetic AlgorithmsWeek 12:Physics Informed Neural Networks for forward and inverse problems
Taught by
Prof. Balaji Srinivasan
