| Sl.No | Chapter Name | MP4 Download |
|---|---|---|
| 1 | Introduction to inverse problems | Download |
| 2 | Fermi estimation | Download |
| 3 | Forward/Direct and Inverse problems | Download |
| 4 | Key drivers for studying inverse methods in engineering | Download |
| 5 | Formulation for inverse problems | Download |
| 6 | Statistical tools for estimation | Download |
| 7 | Statistical description of errors | Download |
| 8 | Well-posed and ill-posed problems | Download |
| 9 | Probability and Statistics Brief over view-I | Download |
| 10 | Probability and Statistics Brief over view-II | Download |
| 11 | Gaussian distribution | Download |
| 12 | Gaussian distribution (Contd.), and Maximum Likelihood Estimation (MLE) | Download |
| 13 | Linear least square regression | Download |
| 14 | Linear least square regression (contd.). | Download |
| 15 | Alternatives to Linear least square. | Download |
| 16 | Polynomial regression | Download |
| 17 | Inverse problems in transient conduction-I | Download |
| 18 | Inverse problems in transient conduction-II | Download |
| 19 | Non-linear regression | Download |
| 20 | Gauss-Newton algorithm (GNA) | Download |
| 21 | Gauss-Newton algorithm (GNA) Example. | Download |
| 22 | Levenberg-Marquardt algorithm (LMA) | Download |
| 23 | Tikhonov regularization | Download |
| 24 | Jacobian and its calculation | Download |
| 25 | Bayesian methods | Download |
| 26 | Bayesian methods contd.., | Download |
| 27 | Metropolis-Hastings algorithm (MH) and Markov Chain Monte Carlo Methods (MCMC) | Download |
| 28 | Introduction to machine learning in heat transfer | Download |
| 29 | Overview of machine learning | Download |
| 30 | Calculation in a neural network model | Download |
| 31 | Gradient Descent method | Download |
| 32 | Gradient Descent method contd.., | Download |
| 33 | Back propagation | Download |
| 34 | Neural network as a surrogate forward model | Download |
| 35 | PINN for an inverse problem | Download |
| 36 | PINN for an inverse problem contd.., | Download |
| 37 | Inverse methods in heat transfer -Summary | Download |
| Sl.No | Chapter Name | English |
|---|---|---|
| 1 | Introduction to inverse problems | PDF unavailable |
| 2 | Fermi estimation | PDF unavailable |
| 3 | Forward/Direct and Inverse problems | PDF unavailable |
| 4 | Key drivers for studying inverse methods in engineering | PDF unavailable |
| 5 | Formulation for inverse problems | PDF unavailable |
| 6 | Statistical tools for estimation | PDF unavailable |
| 7 | Statistical description of errors | PDF unavailable |
| 8 | Well-posed and ill-posed problems | PDF unavailable |
| 9 | Probability and Statistics Brief over view-I | PDF unavailable |
| 10 | Probability and Statistics Brief over view-II | PDF unavailable |
| 11 | Gaussian distribution | PDF unavailable |
| 12 | Gaussian distribution (Contd.), and Maximum Likelihood Estimation (MLE) | PDF unavailable |
| 13 | Linear least square regression | PDF unavailable |
| 14 | Linear least square regression (contd.). | PDF unavailable |
| 15 | Alternatives to Linear least square. | PDF unavailable |
| 16 | Polynomial regression | PDF unavailable |
| 17 | Inverse problems in transient conduction-I | PDF unavailable |
| 18 | Inverse problems in transient conduction-II | PDF unavailable |
| 19 | Non-linear regression | PDF unavailable |
| 20 | Gauss-Newton algorithm (GNA) | PDF unavailable |
| 21 | Gauss-Newton algorithm (GNA) Example. | PDF unavailable |
| 22 | Levenberg-Marquardt algorithm (LMA) | PDF unavailable |
| 23 | Tikhonov regularization | PDF unavailable |
| 24 | Jacobian and its calculation | PDF unavailable |
| 25 | Bayesian methods | PDF unavailable |
| 26 | Bayesian methods contd.., | PDF unavailable |
| 27 | Metropolis-Hastings algorithm (MH) and Markov Chain Monte Carlo Methods (MCMC) | PDF unavailable |
| 28 | Introduction to machine learning in heat transfer | PDF unavailable |
| 29 | Overview of machine learning | PDF unavailable |
| 30 | Calculation in a neural network model | PDF unavailable |
| 31 | Gradient Descent method | PDF unavailable |
| 32 | Gradient Descent method contd.., | PDF unavailable |
| 33 | Back propagation | PDF unavailable |
| 34 | Neural network as a surrogate forward model | PDF unavailable |
| 35 | PINN for an inverse problem | PDF unavailable |
| 36 | PINN for an inverse problem contd.., | PDF unavailable |
| 37 | Inverse methods in heat transfer -Summary | PDF unavailable |
| Sl.No | Language | Book link |
|---|---|---|
| 1 | English | Not Available |
| 2 | Bengali | Not Available |
| 3 | Gujarati | Not Available |
| 4 | Hindi | Not Available |
| 5 | Kannada | Not Available |
| 6 | Malayalam | Not Available |
| 7 | Marathi | Not Available |
| 8 | Tamil | Not Available |
| 9 | Telugu | Not Available |