Interactive models: a blessing or a curse in disguise?

While many models can predict performance with incredible precision, the true challenge lies in optimizing them effectively. Though powerful, relying solely on human intuition and interactive interfaces may lead to inconsistent outcomes, chaotic workflows, and missed opportunities. How do you ensure you’re not only finding the ‘best’ solution but exploring the full spectrum of possibilities? The answer lies in embracing PIDO, where rigorous tools take over and guide you through even the most complex parameter landscapes—revealing insights beyond the obvious.

Model interaction is only possible if an interface is in place. The most engaging type is a fully interactive graphical user interface (GUI) linked directly to the model. Typically, there are fields for input parameters and outputs. You can make adjustments, click some buttons, and inspect the matching results generated by the model.

At Verhaert, we’ve developed many such models where interactivity allows users to freely explore and adjust parameters as quickly as new concepts emerge. However, the downside is that the optimization control is in the hands of human intuition, leading to a chaotic process, often where repeated trials yield different results. Thus, while interactivity can be a blessing, it can also be a curse for rigorous optimization crucial for informed business decisions.

So remember: optimization is a rigorous task best left to accurate programs. The most crucial feature of your model isn’t a flashy GUI, but a modest scripting interface that another program can control.

One tool to rule them all

Programs that utilize this scripting interface for design exploration and optimization are referred to as PIDO (Process Integration and Design Optimization) tools. These programs operate at a meta-level and perform three core tasks:

1. Systematically varying parameters within a user-defined parameter space.
2. Running an external model (or a chain of models) using these parameters.
3. Saving the results of each run in a structured manner.

For example, Excel’s “scenario manager” and “data table” functions allow you to evaluate functions across different cases and summarize the results in a table.

Dedicated PIDO packages such as optiSLang (Ansys), HEEDS (Siemens), Isight (Simulia), and Optimus (3rd party) offer even more advanced features handling everything from spreadsheets and Matlab scripts to finite element (FE) simulations or a combination of them. These packages also come with optimization algorithms and extensive data visualization suites right out of the box.

If you would rather have full control in developing a PIDO script, you can quickly assemble it in Python using open-source libraries. Our recommended libraries include:

• Systematically varying parameters and saving the results: psweep or pypet
• Interfacing with models written in other programs: Python Subprocess
• Optimizing objective functions: DEAP (evolutionary)
• Visualizing data: matplotlib or seaborn (mostly static) / Plotly (interactive)

Once created, a custom PIDO script can be reused for any model with minimal modifications.

In short, if your model has a scripting interface, PIDO software can leverage it to optimize designs with minimal manual input. If you have a license for FE software, chances are a PIDO program comes prepackaged with it; and if you don’t, it’s easy to cook up in a language like Python. So why not give it a try? 

The post ‘Pareto Front’ and beyond: discovering the art of smart simulation & modeling appeared first on Verhaert Masters in Innovation.

The post ‘Pareto Front’ and beyond: discovering the art of smart simulation & modeling appeared first on Verhaert Masters in Innovation.