The Oracle of DLphi. - Dominik Alfke; Weston Baines; Jan Blechschmidt; Mauricio J. del Razo Sarmina; Amnon Drory; Dennis Elbrächter; Nando Farchmin; Matteo Gambara; Silke Glas; Philipp Grohs; Peter Hinz; Danijel Kivaranovic; et al. (CoRR, 2019)

Invertible Neural Networks Versus MCMC for Posterior Reconstruction in Grazing Incidence X-Ray Fluorescence. - Anna Andrle; Nando Farchmin; Paul Hagemann; Sebastian Heidenreich; Victor Soltwisch; Gabriele Steidl (SSVM, 2021)

Efficient approximation of high-dimensional exponentials by tensornetworks. - Martin Eigel; Nando Farchmin; Sebastian Heidenreich; Philipp Trunschke (CoRR, 2021)

Adaptive non-intrusive reconstruction of solutions to high-dimensional parametric PDEs. - Martin Eigel; Nando Farchmin; Sebastian Heidenreich; Philipp Trunschke (CoRR, 2021)

Convergence of adaptive Galerkin FEM for parametric PDEs with lognormal coefficients. - Martin Eigel; Nando Farchmin (CoRR, 2023)

Adaptive Nonintrusive Reconstruction of Solutions to High-Dimensional Parametric PDEs. - Martin Eigel; Nando Farchmin; Sebastian Heidenreich; Philipp Trunschke (SIAM J. Sci. Comput., 2023)

PyThia: A Python package for Uncertainty Quantification based on non-intrusive polynomial chaos expansions. - Nando Hegemann; Sebastian Heidenreich (J. Open Source Softw., 2023)

Machine-learning for photoplethysmography analysis: Benchmarking feature, image, and signal-based approaches. - Mohammad Moulaeifard; Loic Coquelin; Mantas Rinkevicius; Andrius Solosenko; Oskar Pfeffer; Ciaran Bench; Nando Hegemann; Sara Vardanega; Manasi Nandi; Jordi Alastruey; Christian Heiss; Vaidotas Marozas; et al. (CoRR, 2025)