Sparse identification of truncation errors. - Stephan Thaler; Ludger Paehler; Nikolaus A. Adams (J. Comput. Phys., 2019)

Reverse-mode automatic differentiation and optimization of GPU kernels via enzyme. - William S. Moses; Valentin Churavy; Ludger Paehler; Jan Hückelheim; Sri Hari Krishna Narayanan; Michel Schanen; Johannes Doerfert (SC, 2021)

Scalable Automatic Differentiation of Multiple Parallel Paradigms through Compiler Augmentation. - William S. Moses; Sri Hari Krishna Narayanan; Ludger Paehler; Valentin Churavy; Michel Schanen; Jan Hückelheim; Johannes Doerfert; Paul D. Hovland (SC, 2022)

Transparent Checkpointing for Automatic Differentiation of Program Loops Through Expression Transformations. - Michel Schanen; Sri Hari Krishna Narayanan; Sarah Williamson; Valentin Churavy; William S. Moses; Ludger Paehler (ICCS (3), 2023)

On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods. - Artur P. Toshev; Ludger Paehler; Andrea Panizza; Nikolaus A. Adams (CoRR, 2023)

ComPile: A Large IR Dataset from Production Sources. - Aiden Grossman; Ludger Paehler; Konstantinos Parasyris; Tal Ben-Nun; Jacob Hegna; William S. Moses; Jose Manuel Monsalve Diaz; Mircea Trofin; Johannes Doerfert (CoRR, 2023)

Koopman-Assisted Reinforcement Learning. - Preston Rozwood; Edward Mehrez; Ludger Paehler; Wen Sun; Steven L. Brunton (CoRR, 2024)

HydroGym: A Reinforcement Learning Platform for Fluid Dynamics. - Christian Lagemann; Ludger Paehler; Jared Callaham; Sajeda Mokbel; Samuel Ahnert; Kai Lagemann; Esther Lagemann; Nikolaus A. Adams; Steven L. Brunton (L4DC, 2025)