Finite

Everyone has their preferred method for writing and debugging code. I like to write code in Jupyter notebook format, executing and debugging it block by block. However, this approach can become visually cluttered and hard to follow as complexity increases, making it challenging for others to understand the code during collaboration. Therefore, I think writing code in .py file format is the best ..

Today, I plan to make some modifications to the existing DNN model coda and address the overfitting issue that has occurred. Until now, training has been conducted using the CPU, but PyTroch allows for model training using the GPU. In fact, setting up this environment involved numberous version mismatch errors, leading to several rebuilds of the conda envirionment. It is recommended to carefully..

In the previous post(100_DNN Example using PyTorch), I explored solving the MNIST classification problem with a DNN that added three hidden layers to an ANN. During the training, while the Training loss decreased normally, the Validation loss decreased to a certain level and then began to increase. This was mentioned as a likely occurrence of overfitting. In this post, I will discuss what overfi..

In a previous post, we implemented an ANN (Artificial Neural Network) with a simple single hidden layer. In this post, we aim to solve a classification problem using a DNN (Deep Neural Network) with more than two hidden layers, continuing with the MNIST dataset used before. First, let's import the necessary libraries. import torch import torchvision import torchvision.transforms as transforms fr..

In this post, we will conduct a simple CNN (Convolutional Neural Network) example in a PyTorch environment using the MNIST dataset. In a previous post (97_Simple DNN Example Using PyTorch), we addressed the same classification problem using a DNN (Deep Neural Network), and with slight modifications, it can be adapted to a CNN. For an explanation of CNN, please refer to another post (69_Convoluti..

In the last post(97_Simple ANN Example Using PyTorch), we conducted an example of classifying MNIST data using a simple ANN. In this post, our goal is to achieve better loss values by modifying different parameters and experimenting with various hyperparameter adjustments. Before proceeding, it was noted that in the last post, the 'manual_seed' value was not set, leading to different loss values..

Having previously focused on thoretical content in the deep learning category, I plan to conduct an example. In this post I will perform a simple ANN (Artificial Neural Network) example in a PyTorch environment using the MNIST dataset. MNIST is a large-scale dataset composed of handwritten digits and is widely used in the fields of machine learning and computer vision for training and testing im..

In this post, I aim to solve an example that involves determining the temperature at each point along an insulated rod with a total lengh $L$. The left end is fixed at ${100^ \circ }{\text{C}}$, and a heat flux of $q = 5000{\text{W/}}{{\text{m}}^2}$ acts on the right end. Here, ${K_{xx}}$ is ${\text{6W/(m}}{{\text{ - }}^{\text{o}}}{\text{C)}}$, and the cross-section is a circle with an area of $..

In a previous post, I theoretically covered the method for calculating the stiffness matrix of a tetrahedron. In this post, based on that theory, I aim to derive the stiffness matrix of a tetrahedron through an example. The given tetrahedral element is as follows. In the given element, $E = 200{\text{GPa}},\,v = 0.3$. To calculate the element stiffness matrix first, it is necessary to determine ..

In the field of structural analysis and Finite Element Analysis (FEA), I intend to explain the process of deriving the stiffness matrix for a basic three-node bar element. Unlike previous postings(39_High Order Example), this time I plan to calculate the stiffness matrix of a three-node bar element using two-point Gaussian integration. A bar element with three nodes and two Gaussian points is as..