Modules / Lectures

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 |