TreeLearn: A Comprehensive Deep Learning Method for Segmenting Individual Trees from Forest Point Clouds. - Jonathan Henrich; Jan van Delden; Dominik Seidel; Thomas Kneib; Alexander S. Ecker (CoRR, 2023)

Vibroacoustic Frequency Response Prediction with Query-based Operator Networks. - Jan van Delden; Julius Schultz; Christopher Blech; Sabine C. Langer; Timo Lüddecke (CoRR, 2023)

Learning to Predict Structural Vibrations. - Jan van Delden; Julius Schultz; Christopher Blech; Sabine C. Langer; Timo Lüddecke (NeurIPS, 2024)

Towards general deep-learning-based tree instance segmentation models. - Jonathan Henrich; Jan van Delden (CoRR, 2024)

Data Publishing in Mechanics and Dynamics: Challenges, Guidelines, and Examples from Engineering Design. - Henrik Ebel; Jan van Delden; Timo Lüddecke; Aditya Borse; Rutwik Gulakala; Marcus Stoffel; Manish Yadav; Merten Stender; Leon Schindler; Kristin Miriam de Payrebrune; Maximilian Raff; C. David Remy; et al. (CoRR, 2024)

The impact of AI on engineering design procedures for dynamical systems. - Kristin Miriam de Payrebrune; Kathrin Flaßkamp; Tom Ströhla; Thomas Sattel; Dieter Bestle; Benedict Röder; Peter Eberhard; Sebastian Peitz; Marcus Stoffel; Rutwik Gulakala; Aditya Borse; Meike Wohlleben; et al. (CoRR, 2024)

TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds. - Jonathan Henrich; Jan van Delden; Dominik Seidel; Thomas Kneib; Alexander S. Ecker (Ecol. Informatics, 2024)

Minimizing Structural Vibrations via Guided Flow Matching Design Optimization. - Jan van Delden; Julius Schultz; Sebastian Rothe; Christian Libner; Sabine C. Langer; Timo Lüddecke (CoRR, 2025)