| Sl.No | Chapter Name | MP4 Download |
|---|---|---|
| 1 | Introduction to the Inverse Methods in Heat Transfer Course | Download |
| 2 | Inverse Problems - Definition, History and Applications | Download |
| 3 | The inverse problem solving process | Download |
| 4 | Review of Basic Heat Transfer for this course | Download |
| 5 | INTRODUCTION TO WEEK 02 | Download |
| 6 | Introduction to Linear Regression for Inverse Problems | Download |
| 7 | Example Application of Linear regression for an inverse conduction problem | Download |
| 8 | Goodness of Fit and Coefficient of Determination | Download |
| 9 | Linear Regression with Quadratic Model | Download |
| 10 | SUMMARY OF WEEK 02 | Download |
| 11 | INTRODUCTION TO WEEK 03 | Download |
| 12 | Introduction to Normal Equations for linear models | Download |
| 13 | Normal Equations for linear models (contd) | Download |
| 14 | Parity Plots | Download |
| 15 | Programming Inverse Methods using Normal Equations | Download |
| 16 | Variants on the Linear Model for inverse problems | Download |
| 17 | SUMMARY OF WEEK 03 | Download |
| 18 | The General Inverse Methods Process | Download |
| 19 | Simple nonlinear inverse problem -- Transient Heat transfer | Download |
| 20 | Review of required calculus results | Download |
| 21 | Gradient Descent Algorithm | Download |
| 22 | Gradient Descent -- Simple Example | Download |
| 23 | Gradient Descent for Nonlinear Inverse Problem -- Theory | Download |
| 24 | Gradient Descent for Nonlinear Inverse Problem -- Coding Example | Download |
| 25 | Newton Algorithm for a System of Equations | Download |
| 26 | Gauss Newton Algorithm -- Derivation and Code | Download |
| 27 | Overfitting and Regularization for Linear Models | Download |
| 28 | Tikhonov Regularization and Levenberg-Marquardt -- Theory | Download |
| 29 | Tikhonov and Levenberg-Marquardt -- Example Code | Download |
| 30 | Introduction to Probability for Inverse Methods | Download |
| 31 | Sum and Product Rules of Probability | Download |
| 32 | Bayes Theorem -- Simple Examples | Download |
| 33 | Independence and Expectation | Download |
| 34 | Variance and Covariance | Download |
| 35 | Gaussian distribution and the standard normal table | Download |
| 36 | Maximum Likelihood Estimate | Download |
| 37 | MLE, MAP estimates | Download |
| 38 | Introduction to Bayesian Methods for Inverse Problems | Download |
| 39 | Offline Bayesian Estimation | Download |
| 40 | Offline Bayesian Estimation -- MATLAB Demo | Download |
| 41 | MHMCMC for Inverse Problems | Download |
| 42 | MHMCMC for Inverse Problems -- MATLAB Demo | Download |
| 43 | Why Machine Learning in Inverse Heat Transfer? | Download |
| 44 | Overview of AI and ML | Download |
| 45 | Supervised Machine Learning as an Inverse Problem | Download |
| 46 | Introduction to Week 9 - From Linear Models to Neural Networks | Download |
| 47 | Gradient Descent - Batch, Stochastic and Mini Batch | Download |
| 48 | Logistic Regression - The Forward Model | Download |
| 49 | Logistic Regression - Binary Entropy Cost Function and Gradient | Download |
| 50 | Multiclass Classification | Download |
| 51 | Linear Separability and Neural Networks | Download |
| 52 | Introduction to Week 10 - XOR and Deeper networks | Download |
| 53 | Forward pass through a simple neural network | Download |
| 54 | Backprop in a scalar chain | Download |
| 55 | Backprop in a MLP | Download |
| 56 | Introduction to Week 11-- ANNs as Surrogate models | Download |
| 57 | Physics Informed Neural Networks -- Introduction | Download |
| 58 | Physics Informed Neural Networks -- an intuitive explanation | Download |
| 59 | Physics Informed Neural Networks -- BC incorporation | Download |
| 60 | PINNs for inverse problems | Download |
| 61 | Introduction to Week 12-- Sensitivity Analysis | Download |
| 62 | Code Examples of Logistic Regression -- OR and AND gates | Download |
| 63 | Code Example of shallow neural network -- XOR gate | Download |
| 64 | Code walkthrough for PINNs in Burgers equation | Download |
| 65 | Formulation of a PINN based inverse problem in unsteady conduction | Download |
| 66 | Formulation of a surrogate model based inverse solution in unsteady conduction | Download |
| 67 | Summary of course | Download |
| Sl.No | Chapter Name | English |
|---|---|---|
| 1 | Introduction to the Inverse Methods in Heat Transfer Course | Download Verified |
| 2 | Inverse Problems - Definition, History and Applications | Download Verified |
| 3 | The inverse problem solving process | Download Verified |
| 4 | Review of Basic Heat Transfer for this course | Download Verified |
| 5 | INTRODUCTION TO WEEK 02 | Download Verified |
| 6 | Introduction to Linear Regression for Inverse Problems | Download Verified |
| 7 | Example Application of Linear regression for an inverse conduction problem | Download Verified |
| 8 | Goodness of Fit and Coefficient of Determination | Download Verified |
| 9 | Linear Regression with Quadratic Model | Download Verified |
| 10 | SUMMARY OF WEEK 02 | Download Verified |
| 11 | INTRODUCTION TO WEEK 03 | Download Verified |
| 12 | Introduction to Normal Equations for linear models | Download Verified |
| 13 | Normal Equations for linear models (contd) | Download Verified |
| 14 | Parity Plots | Download Verified |
| 15 | Programming Inverse Methods using Normal Equations | Download Verified |
| 16 | Variants on the Linear Model for inverse problems | Download Verified |
| 17 | SUMMARY OF WEEK 03 | Download Verified |
| 18 | The General Inverse Methods Process | PDF unavailable |
| 19 | Simple nonlinear inverse problem -- Transient Heat transfer | PDF unavailable |
| 20 | Review of required calculus results | PDF unavailable |
| 21 | Gradient Descent Algorithm | PDF unavailable |
| 22 | Gradient Descent -- Simple Example | PDF unavailable |
| 23 | Gradient Descent for Nonlinear Inverse Problem -- Theory | PDF unavailable |
| 24 | Gradient Descent for Nonlinear Inverse Problem -- Coding Example | PDF unavailable |
| 25 | Newton Algorithm for a System of Equations | PDF unavailable |
| 26 | Gauss Newton Algorithm -- Derivation and Code | PDF unavailable |
| 27 | Overfitting and Regularization for Linear Models | PDF unavailable |
| 28 | Tikhonov Regularization and Levenberg-Marquardt -- Theory | PDF unavailable |
| 29 | Tikhonov and Levenberg-Marquardt -- Example Code | PDF unavailable |
| 30 | Introduction to Probability for Inverse Methods | PDF unavailable |
| 31 | Sum and Product Rules of Probability | PDF unavailable |
| 32 | Bayes Theorem -- Simple Examples | PDF unavailable |
| 33 | Independence and Expectation | PDF unavailable |
| 34 | Variance and Covariance | PDF unavailable |
| 35 | Gaussian distribution and the standard normal table | PDF unavailable |
| 36 | Maximum Likelihood Estimate | PDF unavailable |
| 37 | MLE, MAP estimates | PDF unavailable |
| 38 | Introduction to Bayesian Methods for Inverse Problems | PDF unavailable |
| 39 | Offline Bayesian Estimation | PDF unavailable |
| 40 | Offline Bayesian Estimation -- MATLAB Demo | PDF unavailable |
| 41 | MHMCMC for Inverse Problems | PDF unavailable |
| 42 | MHMCMC for Inverse Problems -- MATLAB Demo | PDF unavailable |
| 43 | Why Machine Learning in Inverse Heat Transfer? | PDF unavailable |
| 44 | Overview of AI and ML | PDF unavailable |
| 45 | Supervised Machine Learning as an Inverse Problem | PDF unavailable |
| 46 | Introduction to Week 9 - From Linear Models to Neural Networks | PDF unavailable |
| 47 | Gradient Descent - Batch, Stochastic and Mini Batch | PDF unavailable |
| 48 | Logistic Regression - The Forward Model | PDF unavailable |
| 49 | Logistic Regression - Binary Entropy Cost Function and Gradient | PDF unavailable |
| 50 | Multiclass Classification | PDF unavailable |
| 51 | Linear Separability and Neural Networks | PDF unavailable |
| 52 | Introduction to Week 10 - XOR and Deeper networks | PDF unavailable |
| 53 | Forward pass through a simple neural network | PDF unavailable |
| 54 | Backprop in a scalar chain | PDF unavailable |
| 55 | Backprop in a MLP | PDF unavailable |
| 56 | Introduction to Week 11-- ANNs as Surrogate models | PDF unavailable |
| 57 | Physics Informed Neural Networks -- Introduction | PDF unavailable |
| 58 | Physics Informed Neural Networks -- an intuitive explanation | PDF unavailable |
| 59 | Physics Informed Neural Networks -- BC incorporation | PDF unavailable |
| 60 | PINNs for inverse problems | PDF unavailable |
| 61 | Introduction to Week 12-- Sensitivity Analysis | PDF unavailable |
| 62 | Code Examples of Logistic Regression -- OR and AND gates | PDF unavailable |
| 63 | Code Example of shallow neural network -- XOR gate | PDF unavailable |
| 64 | Code walkthrough for PINNs in Burgers equation | PDF unavailable |
| 65 | Formulation of a PINN based inverse problem in unsteady conduction | PDF unavailable |
| 66 | Formulation of a surrogate model based inverse solution in unsteady conduction | PDF unavailable |
| 67 | Summary of course | 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 |