In a recent interview with Engineering.com, Bjorn Sjodin, senior vice president of product management at Comsol, shared insights on the burgeoning role of artificial intelligence (AI) in simulation, as well as broader trends expected to shape the landscape by 2025. Comsol, known for its simulation software, has recently launched Comsol Multiphysics 6.3, which brings enhanced capabilities and features to its users.
Sjodin highlighted the beneficial applications of AI chatbots in assisting novice simulation engineers. He stated, “It’s like having a tutor that you can ask basic simulation questions,” illustrating how these tools can provide guidance on common issues such as selecting appropriate boundary conditions in heat transfer simulations. Though he noted that the chatbots can sometimes offer incorrect information, their utility for beginners is evident. “If it's something that is common simulation knowledge by experienced engineers, then the chatbot will probably know some of that,” he added.
A notable feature of the latest Comsol iteration is its integration with ChatGPT, which Sjodin views as a pivotal tool for both education and efficiency. He elaborated on how AI can streamline programming tasks for users by generating snippets of code or assisting in debugging, thus enhancing productivity. The possibility of these chatbots evolving to autonomously create complete applications was also discussed, with Sjodin stating, “Yes, I think so. That’s where everything is headed.” However, he cautioned that a major limitation remains in their understanding of spatial features in CAD models, suggesting that significant improvements in AI’s spatial perception are necessary before achieving comprehensive automation in modeling tasks.
Looking ahead, Sjodin pointed out that the trend of reduced order modeling and the use of surrogate models is gaining traction within the simulation community. These methods allow engineers to significantly simplify complex simulations, reducing computational time from hours to seconds while still delivering comparable accuracy. This trend is driven by the industry’s increasing demand for efficient digital twins and rapid simulation applications.
When discussing traditional technologies related to reduced order modeling, Sjodin described methods that streamline large matrix systems. For example, advanced techniques can condense a 10 million by 10 million matrix down to a far smaller size, preserving essential data while improving the speed of analysis. Moreover, he noted that while neural networks are emerging as valuable tools, traditional simplified modeling techniques remain relevant and effective for certain applications.
On the subject of neural network flexibility, he explained how these models perform well in scenarios involving numerous parameters, provided that they are appropriately trained. “As long as you’re staying within those parametric ranges that you pre-trained the model for, it is very good,” Sjodin commented, indicating that response times can vastly improve to mere seconds compared to traditional methods that may require hours of computation.
The increasing reliance on fast-response simulation solutions is particularly crucial for operators on factory floors or in production contexts, where immediate results are essential for operational effectiveness. As the simulation landscape continues to evolve, the integration of AI, reduced order modeling, and user-friendly interfaces promise to transform business practices, enhancing efficiency and accessibility across diverse engineering disciplines.
Source: Noah Wire Services