Mathias Niepert is a Professor at the Institute for Artificial Intelligence within the Faculty of Computer Science, Electrical Engineering and Information Technology at the University of Stuttgart. His research focuses on advancing machine learning techniques with applications in scientific computing, graph neural networks, and medical imaging. He is particularly known for contributions to physics-informed neural networks, equivariant models, and graph learning frameworks. Key research areas include: Scientific Machine Learning for PDEs and molecular modeling Graph neural networks and their theoretical limitations Medical vision-language models and multimodal learning Efficient neural network architectures (transformers, FNOs) Domain knowledge integration in deep learning His work often bridges theoretical foundations with practical applications, as evidenced by extensive publications (2018–2025) on topics like adaptive message passing, equivariant networks, and medical imaging systems. He has contributed to benchmark development through initiatives like PDEBench and pioneered methods for equivariant diffusion models and molecular representation learning. His current projects emphasize: Improving generalization in Fourier Neural Operators Addressing oversmoothing in graph networks Combining physics principles with neural architectures Medical AI applications through multimodal fusion
