Designing Better Products with Machine Learning in HyperWorks

Machine learning and artificial intelligence are increasingly referenced in our everyday lives.  The terms have become ubiquitous and as an engineer in the world of simulation, it’s natural to wonder how these technologies will assimilate into our daily work.  Low code or no code platforms such as Altair Knowledge Works make the tools of data science accessible to an increasingly wider audience by lowering the barrier of entry.   These solutions further the goal of democratizing data science, but their nature still requires familiarity with a collection of abstract plots, charts, and metrics.  The Design Explorer workflows in Altair HyperWorks 2021 bring engineers AI insights from the simulated physics directly into their daily working environment.  The seamless integrations bring intuitive analytics with only a few clicks.

The full potential of CAE has always been built on the axiom that crashing virtual cars or bending virtual wings is cheaper than the corresponding physical tests.  Final verification via physical tests is still required, of course, but only to confirm the predicted outcome of the virtual design process.  Through much of its history, CAE had been used primarily as a trial and error process of design improvement.  Scalable computing clusters, including most recently cloud environments, allow for simulation on a scale that was previously unimaginable.  Machine learning techniques are ideal tools to sift through the mountains of data.  An easy to use solution must deliver a user-friendly solution to 3 critical steps of the process.

1 – Define the scope of the problem. The inputs and outputs can be manually defined or automatically collected.  Examples include features such as material, geometry, or loading or Key Performance Indices like stress or mass.

2- Collect virtual samples.  Keeping track of all the data requires a management system and when data doesn’t yet exist, an integrated simulation and data management system is required to synthesize the data on demand.

3- Provide visual explanations.  This piece is key to a natural user experience.  I feel the ultimate goal here is most appropriately expressed by Edward Tufte, a leading expert in the field of information design : “Don’t think.  Look”.

The Design Exploration workflows satisfy each of the 3 requirements.  A dedicated ribbon in the user interface let’s user setup their problem intuitively by clicking directly on the 3d model.

Once the problem scope is well posed, the next step is to manage the simulation and data collection.  Creating a comprehensive sampling strategy, executing the series of simulations, and presenting status of the ongoing process is handled within the user interface.

Finally, the user is presented with easily interpretable analytics that let the user focus on the engineering.  The influence of an input on the design performance are shown directly on the model, using color and saturation to aid visualization.

New in 2021 is the introduction of full simulation predictions to visualize the impact of design changes in real time.  This cutting edge AI learns from the virtual data to give instant insight with no coding or parameter tuning. It is truly automatic machine learning within the reach of any user.

These examples are just the beginning of what we at Altair have called engineering data science.  We have many more ideas for the future. #onlyforward

What are your thoughts on how engineering and data science will continue to evolve?  Let us know in the comments below.