Learn · Inverse Methods in Heat Transfer
Lecture 27 of 37Inverse Methods in Heat Transfer
Metropolis-Hastings algorithm (MH) and Markov Chain Monte Carlo Methods (MCMC)
Prof. C.Balaji and Prof.S. Balaji · IIT Madras
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All the lectures
- Introduction to inverse problems
- Fermi estimation
- Forward/Direct and Inverse problems
- Key drivers for studying inverse methods in engineering
- Formulation for inverse problems
- Statistical tools for estimation
- Statistical description of errors
- Well-posed and ill-posed problems
- Probability and Statistics Brief over view-I
- Probability and Statistics Brief over view-II
- Gaussian distribution
- Gaussian distribution (Contd.), and Maximum Likelihood Estimation (MLE)
- Linear least square regression
- Linear least square regression (contd.).
- Alternatives to Linear least square.
- Polynomial regression
- Inverse problems in transient conduction-I
- Inverse problems in transient conduction-II
- Non-linear regression
- Gauss-Newton algorithm (GNA)
- Gauss-Newton algorithm (GNA) Example.
- Levenberg-Marquardt algorithm (LMA)
- Tikhonov regularization
- Jacobian and its calculation
- Bayesian methods
- Bayesian methods contd..,
- Metropolis-Hastings algorithm (MH) and Markov Chain Monte Carlo Methods (MCMC)
- Introduction to machine learning in heat transfer
- Overview of machine learning
- Calculation in a neural network model
- Gradient Descent method
- Gradient Descent method contd..,
- Back propagation
- Neural network as a surrogate forward model
- PINN for an inverse problem
- PINN for an inverse problem contd..,
- Inverse methods in heat transfer -Summary