With growing demands for efficiency, safety, and innovation, traditional simulation methods are struggling to keep pace. In sectors like aerospace, automotive, and manufacturing, explicit dynamic simulations are vital for analyzing high-impact scenarios. However, they are computationally expensive and operationally complex.
Artificial Intelligence (AI) and Machine Learning (ML) are now transforming simulation methodologies . Geometric Deep Learning (GDL), a subset of these technologies, offers the ability to predict key structural responses such as stress and deformation, without requiring time-intensive simulations. This approach facilitates faster design iterations, cost optimization, and earlier design exploration.
In this whitepaper, we demonstrate the use of PhysicsAI to streamline explicit dynamic simulations, reducing manual effort and computational time significantly. By training models on high-quality simulation data, the framework enables accurate predictions for new geometries within a defined design space. While initial investment in data generation is significant, the long-term gains include accelerated workflows, increased efficiency, and a scalable foundation for intelligent engineering.