3 papers
cs.AI2026
A tensor network formalism for neuro-symbolic AI
Alex Goessmann, Janina Schütte, Maximilian Fröhlich +1
The unification of neural and symbolic approaches to artificial intelligence remains a central open challenge. In this work, we introduce a tensor network formalism, which captures…
cs.LG2025
Multi-level Neural Networks for high-dimensional parametric obstacle problems
Martin Eigel, Cosmas Heiß, Janina E. Schütte
A new method to solve computationally challenging (random) parametric obstacle problems is developed and analyzed, where the parameters can influence the related partial differenti…
cs.LG2024
Sampling from Boltzmann densities with physics informed low-rank formats
Paul Hagemann, Janina Schütte, David Sommer +2
Our method proposes the efficient generation of samples from an unnormalized Boltzmann density by solving the underlying continuity equation in the low-rank tensor train (TT) forma…