Physics Informed Neural Networks (PINNs)

سرفصل های دوره

Simulations with AI

1. Introduction

1. Introduction

2.1 ref.txt

2. Installing Anaconda

3.1 ref.txt

3. Course Structure

2. FDM Numerical Solution 1D Heat Equation

1. Numerical solution theory

2.1 heat main.zip

2. Pre-processing

3.1 heat main.zip

3. Solving the Equation

4.1 heat main.zip

4. Post-processing

3. FDM Numerical Solution for 2D Burgers Equation

1.1 burgers 2d main.zip

1. Pre-processing

2.1 burgers 2d main.zip

2. Solving the Equation

3.1 burgers 2d main.zip

3. Post-processing

4. PINNs Solution for 1D Burgers Equation

1. PINNs Theory

2. Deep Learning Theory

3.1 burgers 1d main.zip

3. Define the Neural Network

4.1 burgers 1d main.zip

4. Initial Conditions and Boundary Conditions

5.1 burgers 1d main.zip

5. Loss Function

6.1 burgers 1d main.zip

6. Train the Model

7.1 burgers 1d main.zip

7. Optimizer

8.1 burgers 1d main.zip

8. Results Evaluation

5. PINNs Solution for 2D Heat Equation

1.1 heat 2d pinns main.zip

1. Define the Neural Network

2.1 heat 2d pinns main.zip

2. Initial Conditions and Boundary Conditions

3.1 heat 2d pinns main.zip

3. Optimizer

4.1 heat 2d pinns main.zip

4. Loss Function

5.1 heat 2d pinns main.zip

5. Train the Model

6.1 heat 2d pinns main.zip

6. Results Evaluation

6. Deepxde Solution for 1D Heat

1.1 deepxde 1d heat main.zip

1. Set Geometry, B.C and I.C

2.1 deepxde 1d heat main.zip

2. Define the Network and the PDE

3.1 deepxde 1d heat main.zip

3. Train the model

4.1 deepxde 1d heat main.zip

4. Result evaluation

7. Deepxde Solution for 2D Navier Stokes

1.1 ns deepxde main.zip

1. Set Geometry

2.1 ns deepxde main.zip

2. Set Boundary Conditions

3.1 ns deepxde main.zip

3. Define the Network and the PDE

4.1 ns deepxde main.zip

4. Train the model

5.1 ns deepxde main.zip

5. Result evaluation

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تولید کننده: Udemy