With the advent of PyAnsys, which offers Python APIs and modules compatible with Ansys products, there has been the possibility of generating numerically accurate data for any experiment or situation. In considering different possibilities for boundary conditions, a strong dataset can be created using PyMAPDL to study structural behavior that can serve as an input to an application’s ML algorithm. The following lesson discusses the capabilities of PyMAPDL and how it helps fulfill the basic need of providing synthetic datasets for machine learning algorithms.
1:00 - Definition of AI/ML & NN
1:33 - Pin Code Reader ML Model Example
2:26 - Classification of ML Algorithm
5:30 - Advantages of PyMAPDL for AI/ML
Get more details on PyAnsys here.