From the 1 of 15 linked papers with an AI index.
15 papers
DeepCormack: Fermi surface tomography using model-based data-driven algorithms
Georg F. B. Lovric, Bryn Drury, Carola-Bibiane Schönlieb +2
The paper introduces DeepCormack, a set of model‑based reconstruction algorithms that combine traditional Cormack methods with supervised deep‑learning models to improve 3D Fermi s…
When is a System Discoverable from Data? Discovery Requires Chaos
Zakhar Shumaylov, Peter Zaika, Philipp Scholl +3
The deep learning revolution has spurred a rise in advances of using AI in sciences. Within physical sciences the main focus has been on discovery of dynamical systems from observa…
FLASH: Flexible Learning of Adaptive Sampling from History in Temporal Graph Neural Networks
Or Feldman, Krishna Sri Ipsit Mantri, Carola-Bibiane Schönlieb +2
Aggregating temporal signals from historic interactions is a key step in future link prediction on dynamic graphs. However, incorporating long histories is resource-intensive. Henc…
Adaptive Correction for Ensuring Conservation Laws in Neural Operators
Chaoyu Liu, Yangming Li, Zhongying Deng +2
Physical laws, such as the conversation of mass and momentum, are fundamental principles in many physical systems. Neural operators have achieved promising performance in learning…
Deep Network Trainability via Persistent Subspace Orthogonality
Alex Massucco, Davide Murari, Carola-Bibiane Schönlieb
Training neural networks via backpropagation is often hindered by vanishing or exploding gradients. In this work, we design architectures that mitigate these issues by analyzing an…
Learning Regularization Functionals for Inverse Problems: A Comparative Study
Johannes Hertrich, Hok Shing Wong, Alexander Denker +16
In recent years, a variety of learned regularization frameworks for solving inverse problems in imaging have emerged. These offer flexible modeling together with mathematical insig…