Introduction to PyMAPDL and ML/AI - Lesson 1

With the advent of PyAnsys, which offers Python APIs and modules compatible with Ansys products, there has been a possibility of generating numerically accurate data for any experiments/situations. Considering numerous possibilities for boundary conditions, a strong dataset can be created using PyMAPDL to study structural behavior, that can serve as an input to the ML algorithm for a concerned application. The following lesson discusses the capabilities of PyMAPDL and how does it help to fulfill the basic need of providing synthetic datasets for machine learning algorithms.

Lecture

Alternate video link.

Video Highlights

0:08 - Introduction

1:00 - Definition of AI/ML & NN

1:33 - Pin Code Reader ML Model Example

2:26 - Classification of ML Algorithm

3:32 - Synthethic Data Set

4:41 - PyMAPDL & AI/ML

5:30 - Advantages of PyMAPDL for AI/ML

7:36 - Summary

Simulation Files

Download the accompanying geometry and archived files here. Get more details on PyAnsys here.