51 citations · 52 across the 4 of their papers we have counts for
4 papers
Architecture-agnostic Lipschitz-constant Bayesian header and its application to resolve semantically proximal classification errors with vision transformers
Frederik Schäfer, Luis Mandl, Lars Kälber +1
Label noise remains a critical bottleneck for the generalization of supervised deep learning models, particularly when errors are structured rather than random. Standard robust tra…
SPINONet: Scalable Spiking Physics-informed Neural Operator for Computational Mechanics Applications
Shailesh Garg, Luis Mandl, Somdatta Goswami +1
Energy efficiency remains a critical challenge in deploying physics-informed operator learning models for computational mechanics and scientific computing, particularly in power-co…
Physics-Informed Time-Integrated DeepONet: Temporal Tangent Space Operator Learning for High-Accuracy Inference
Luis Mandl, Dibyajyoti Nayak, Tim Ricken +1
Accurately modeling and inferring solutions to time-dependent partial differential equations (PDEs) over extended horizons remains a core challenge in scientific machine learning.…
Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine Learning
Luis Mandl, Somdatta Goswami, Lena Lambers +1
The deep operator network (DeepONet) is a popular neural operator architecture that has shown promise in solving partial differential equations (PDEs) by using deep neural networks…