Publications

List of publications within the SPP 2353

Colors

black: published
green: accepted for publication
orange: submitted for publication

2024

Borse, A.; Gulakala, R.; Stoffe, M.: Development of a machine learning-based design optimization method for crashworthiness analysis. Archives of Mechanics, 2024. DOI: 10.24423/aom.4454

Yadav M.; Sinha S.; Stender M.: Evolution beats random chance: Performance-dependent network evolution for enhanced computational capacity, 2024, DOI: 10.48550/arXiv.2403.15869

2023

Röder, B.; Ebel, H.; Eberhard, P.: Towards intelligent design assistants for planar multibody mechanisms. Proceedings in Applied Mathematics and Mechanics, 00, e202300060, 2023. DOI: 10.1002/pamm.202300060

 

Schultz, J.; van Delden, J.; Blech, C.; Langer, S. C.; Lüddecke, T.: Deep learning for frequency response prediction of a multimass oscillator. Proceedings in Applied Mathematics and Mechanics, 00, e202300091, 2023. DOI: 10.1002/pamm.202300091
 

Wohlleben, M.; Muth, L.; Peitz, S.; Sextro, W.: Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits. Proceedings in Applied Mathematics and Mechanics, 00, e202300039, 2023. DOI: 10.1002/pamm.202300039

 
Hotegni, S. S.; Peitz, S.; Berkemeier, M.: Multi-Objective Optimization for Sparse Deep Neural Network Training.
 
Rosa, N.; Katamish, B.; Raff, M.; Remy, C. D.: An Approach for Generating Families of Energetically Optimal Gaits from Passive Dynamic Walking Gaits, 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 8551-8557, Detroit, MI, USA, 2023.  DOI: 10.1109/IROS55552.2023.10342322
 

Gulakala, R.; Markert, B.; Stoffel, M.: Graph Neural Network enhanced Finite Element modelling. Proceedings in Applied Mathematics and Mechanics, 22, e202200306, 2023. DOI: 10.1002/pamm.202200306

Borse, A.; Gulakala, R.; Stoffel, M.: Machine learning enhanced optimisation of crash box design for crashworthiness analysis. Proceedings in Applied Mathematics and Mechanics, 23, e202300145, 2023. DOI: 10.1002/pamm.202300145

Gulakala, R.; Markert, B.; Stoffel, M.: Generative learning-based model for the prediction of 2D stress distribution. Proceedings in Applied Mathematics and Mechanics, 23, e202300201, 2023. DOI: 10.1002/pamm.202300201

Hoffmann, M. K.; Gulakala, R.; Mühlenhoff, J.; Ding, Z.; Sattel, T.; Stoffel, M.; Flaßkamp, K.: Data augmentation for design of concentric tube continuum robots by generative adversarial networks. Proceedings in Applied Mathematics and Mechanics, 23, e202300278, 2023. DOI: 10.1002/pamm.202300278

Gulakala, R.; Markert, B.; Stoffel, M.: Physics informed Spiking Generative Adversarial Networks in Structural Mechanics, 2nd IACM conference on Mechanistic Machine Learning and Digital Engineering for Computational Science Engineering and Technology (MMLDE|CEST), El Paso, TX, USA, 2023. DOI: 10.26226/m.64c26777632e9539aa87d755

2022

Raff, M.; Rosa, N.; Remy, C. D.: Generating Families of Optimally Actuated Gaits from a Legged System's Energetically Conservative Dynamics. International Conference on Intelligent Robots and Systems (IROS), p. 8866-8872, Kyoto, Japan, 2022. DOI: 10.1109/IROS47612.2022.9981693

To the top of the page