Machine learning (ML) and artificial intelligence (AI) are hot topics in the engineering simulation community. These techniques have applications across the product lifecycle.
For example, a design engineer can train an AI system to find optimal designs using data from simulations. AI-assisted simulations can also be used for many other purposes such as manufacturing process control, predictive maintenance, tolerance analysis, and computational risk analysis.
However, using ML and AI for simulation poses several major challenges. Many engineering simulations are deterministic, but the underlying problems they model are subject to uncertainties and, therefore, are stochastic in nature. Although AI may produce an optimal solution, it could be one that corresponds to an unrealistic scenario rather than the desired solution incorporating real-world uncertainty.
To achieve its true aim, the AI system must be trained in the stochastic nature of the outcomes of interest by incorporating uncertainty into its decision rules. Other challenges include how to understand uncertainties in ML and AI models themselves and how to build such models for data sets with many inputs or with spatially and/or temporally varying outputs, e.g. a CFD simulation that produces temperature as a function of location.
This 60-minute Webinar discusses the methods needed to address the challenges involved with building ML and AI models for engineering simulations.
An audience Q&A follows the technical presentation.
