Workflows in Ansys – similarities to artificial intelligence

Let’s start with a classification. Similar to an AI like ChatGPT, the quality of the prediction of a simulation model depends largely on having a good database, which then needs to be trained. As engineers, however, we don't have big data with billions of parameters. In the worst case, we only have a few hundred variants of a design as well as their simulation results describing the physical behavior that form the database. But this is quite manageable in comparison. In our case, we’re able to bend and twist our spoon in all possible directions using the parameters we have. It’s very simple: just a click on the parameter box and the values can be changed using the parameter set (see picture below).

The database is created when we generate and calculate variants using these parameters. Initially, it’s difficult to interpret the resulting maximum deformation values. However, images can help. Did you know that you can save an additional image for each variant? This can be done with APDL, for example:

What is important for a trained data model (DL) ?

Next, we can use this data – let’s call it the deep learning phase – to train a metamodel that ultimately predicts all conceivable maximum deformations. Is the deformation of the spoon tip greater when equal force is applied to the side as opposed to the back? How far will it bend if it is twisted with a moment at the same time? Thanks to our metamodel, we are now able to provide this answer live and without further simulation. Three points are important here:

• Data security: We want to protect the generated data. Nothing should be sent and processed over the Internet. Instead, everything should be kept in our computing environment. No one else should know your spoon secret.
• Prediction quality: How good is my trained model? Does it just deliver a good-looking result? To evaluate the quality, we can use the so-called coefficient of prognosis, which tells us whether the model can be used or whether it should be trained further.
• Traceability: What logic does AI use to arrive at its answer? This is also an issue we need to consider here. The metamodel explains the relationships between the parameters, which contributes to understanding when interpreting the results. In other words: transparency – not a black box