How do particle physicists learn the programming concepts they need? - Stefan Kluth; Maria Grazia Pia; Thomas Schörner-Sadenius; Peter Steinbach (CoRR, 2015)

Porting the MPI Parallelized LES Model PALM to Multi-GPU Systems - An Experience Report. - Helge Knoop; Tobias Gronemeier; Christoph Knigge; Peter Steinbach (ISC Workshops, 2016)

An automated workflow for parallel processing of large multiview SPIM recordings. - Christopher Schmied; Peter Steinbach; Tobias Pietzsch; Stephan Preibisch; Pavel Tomancak (Bioinform., 2016)

gearshifft - The FFT Benchmark Suite for Heterogeneous Platforms. - Peter Steinbach; Matthias Werner 004 (ISC, 2017)

Porting the MPI-parallelised LES model PALM to multi-GPU systems and many integrated core processors - an experience report. - Helge Knoop; Tobias Gronemeier; Matthias Sühring; Peter Steinbach; Matthias Noack; Florian Wende; Thomas Steinke 001; Christoph Knigge; Siegfried Raasch; Klaus Ketelsen (Int. J. Comput. Sci. Eng., 2018)

Teaching Machine Learning in 2020. - Peter Steinbach; Heidi Seibold; Oliver Guhr (Teaching ML, 2020)

Motivation und Konzept zur boden- und luftgestützten Detektion von Virosen in der Pflanzgutproduktion von Stärkekartoffeln. - Julius Kirfel; Stefan Hinck; Juliane Renner; Arshnoor Singh; Peter Steinbach; Arno Ruckelshausen (GIL Jahrestagung, 2021)

A Hybrid Radiomics Approach to Modeling Progression-Free Survival in Head and Neck Cancers. - Sebastian Starke; Dominik Thalmeier; Peter Steinbach; Marie Piraud (HECKTOR@MICCAI, 2021)

Teaching ML in 2021 - An Overview and Introduction. - Oliver Guhr; Katherine M. Kinnaird; Peter Steinbach (Teaching ML, 2021)

Will the sun shine? - An accessible dataset for teaching machine learning and deep learning. - Florian Huber; Erica Dafne van Kuppevelt; Peter Steinbach; Colin Sauze; Yang Liu; Berend Weel (Teaching ML, 2022)

Machine Learning State-of-the-Art with Uncertainties. - Peter Steinbach; Felicita Gernhardt; Mahnoor Tanveer; Steve Schmerler; Sebastian Starke (CoRR, 2022)

Recommendations on test datasets for evaluating AI solutions in pathology. - André Homeyer; Christian Geißler; Lars Ole Schwen; Falk Zakrzewski; Theodore Evans; Klaus Strohmenger; Max Westphal; Roman David Bülow; Michaela Kargl; Aray Karjauv; Isidre Munné-Bertran; Carl Orge Retzlaff; et al. (CoRR, 2022)

Detecting Adversarial Examples in Batches - a geometrical approach. - Danush Kumar Venkatesh; Peter Steinbach (CoRR, 2022)

Providing AI expertise as an infrastructure in academia. - Marie Piraud; Andrés Camero; Markus Götz; Stefan Kesselheim; Peter Steinbach; Tobias Weigel (Patterns, 2023)

Uncertainty Estimation in Instance Segmentation with Star-convex Shapes. - Qasim M. K. Siddiqui; Sebastian Starke; Peter Steinbach (WACV, 2024)

Harnessing Machine Learning for Single-Shot Measurement of Free Electron Laser Pulse Power. - Till Korten; Vladimir Rybnikov; Mathias Vogt; Juliane Roensch-Schulenburg; Peter Steinbach; Najmeh Mirian (CoRR, 2024)

Testing Uncertainty of Large Language Models for Physics Knowledge and Reasoning. - Elizaveta Reganova; Peter Steinbach (CoRR, 2024)

mlphys101 - Exploring the performance of Large-Language Models in multilingual undergraduate physics education. - Marcel Völschow; P. Buczek; P. Carreno-Mosquera; C. Mousavias; S. Reganova; E. Roldan-Rodriguez; Peter Steinbach; A. Strube (RODARE, 2024)

Training data for "Harnessing Machine Learning for Single-Shot Measurement of Free Electron Laser Pulse Power". - Till Korten; Peter Steinbach; Najmeh Mirian (RODARE, 2024)

sbi reloaded: a toolkit for simulation-based inference workflows. - Jan Boelts; Michael Deistler; Manuel Glöckler; Álvaro Tejero-Cantero; Jan-Matthis Lueckmann; Guy Moss 001; Peter Steinbach; Thomas Moreau 001; Fabio Muratore; Julia Linhart; Conor Durkan; Julius Vetter; et al. (J. Open Source Softw., 2025)