Virtual Perception & Learning

The “Virtual Perception & Learning” unit develops next-generation computer-aided methods that combine physical modeling with state-of-the-art machine learning. Our research focuses on enabling reliable, interpretable, and data-efficient characterization of complex technical systems by integrating sparse measurement data, domain expertise, and physically-guided inferences. Our goal is to advance virtual modeling beyond traditional statistical methods and to create physics-based models that remain robust even under data-scarce or rapidly changing conditions.

Our team combines expertise in computational physics and simulation methods, data science, and AI model design. We work at the intersection of theory and application, developing algorithms while collaborating with partners in research and industry to validate our methods in real-world environments.