Estimation of gait mechanics based on simulated and measured IMU data using an artificial neural network M Mundt, A Koeppe, S David, T Witter, F Bamer, W Potthast, B Markert Frontiers in bioengineering and biotechnology 8, 41, 2020 | 125 | 2020 |
Prediction of lower limb joint angles and moments during gait using artificial neural networks M Mundt, W Thomsen, T Witter, A Koeppe, S David, F Bamer, W Potthast, ... Medical & biological engineering & computing 58, 211-225, 2020 | 96 | 2020 |
Efficient numerical modeling of 3D-printed lattice-cell structures using neural networks A Koeppe, CAH Padilla, M Voshage, JH Schleifenbaum, B Markert Manufacturing Letters 15, 147-150, 2018 | 70 | 2018 |
An intelligent nonlinear meta element for elastoplastic continua: deep learning using a new Time-distributed Residual U-Net architecture A Koeppe, F Bamer, B Markert Computer Methods in Applied Mechanics and Engineering 366, 113088, 2020 | 53 | 2020 |
An efficient Monte Carlo strategy for elasto-plastic structures based on recurrent neural networks A Koeppe, F Bamer, B Markert Acta Mechanica 230, 3279-3293, 2019 | 51 | 2019 |
Prediction of ground reaction force and joint moments based on optical motion capture data during gait M Mundt, A Koeppe, S David, F Bamer, W Potthast, B Markert Medical Engineering & Physics 86, 29-34, 2020 | 41 | 2020 |
Artificial neural networks in motion analysis—applications of unsupervised and heuristic feature selection techniques M Mundt, A Koeppe, F Bamer, S David, B Markert Sensors 20 (16), 4581, 2020 | 30 | 2020 |
Explainable artificial intelligence for mechanics: physics-explaining neural networks for constitutive models A Koeppe, F Bamer, M Selzer, B Nestler, B Markert Frontiers in Materials 8, 824958, 2022 | 27* | 2022 |
Intelligent prediction of kinetic parameters during cutting manoeuvres M Mundt, S David, A Koeppe, F Bamer, B Markert, W Potthast Medical & Biological Engineering & Computing 57, 1833-1841, 2019 | 23 | 2019 |
An efficient Monte Carlo simulation strategy based on model order reduction and artificial neural networks F Bamer, A Koeppe, B Markert PAMM 17 (1), 287-288, 2017 | 15 | 2017 |
Neural network representation of a phase‐field model for brittle fracture A Koeppe, F Bamer, CA Hernandez Padilla, B Markert PAMM 17 (1), 253-254, 2017 | 15 | 2017 |
An intelligent meta‐element for linear elastic continua A Koeppe, F Bamer, B Markert PAMM 18 (1), e201800283, 2018 | 12 | 2018 |
Model reduction and submodelling using neural networks. A Koeppe, F Bamer, B Markert PAMM: Proceedings in Applied Mathematics & Mechanics 16 (1), 2016 | 11 | 2016 |
Deep learning in the finite element method A Koeppe Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2021, 2021 | 9 | 2021 |
Prediction of joint kinetics based on joint kinematics using neural networks M Mundt, A Koeppe, F Bamer, W Potthast, B Markert Proceedings of the 36th Conference of the International Society of …, 2018 | 9 | 2018 |
Machine learning assisted design of experiments for solid state electrolyte lithium aluminum titanium phosphate Y Zhao, N Schiffmann, A Koeppe, N Brandt, EC Bucharsky, KG Schell, ... Frontiers in Materials 9, 821817, 2022 | 7 | 2022 |
Prediction of joint kinetics based on joint kinematics using artificial neural networks M Mundt, A Koeppe, F Bamer, W Potthast, B Markert ISBS Proceedings Archive 36 (1), 794, 2018 | 7 | 2018 |
Characterization of porous membranes using artificial neural networks Y Zhao, P Altschuh, J Santoki, L Griem, G Tosato, M Selzer, A Koeppe, ... Acta Materialia 253, 118922, 2023 | 6 | 2023 |
Mechanik 4.0. Künstliche Intelligenz zur Analyse mechanischer Systeme A Koeppe, DF Hesser, M Mundt, F Bamer, B Markert Handbuch Industrie 4.0: Recht, Technik, Gesellschaft, 553-567, 2020 | 5 | 2020 |
Data‐Driven Virtual Material Analysis and Synthesis for Solid Electrolyte Interphases D Rajagopal, A Koeppe, M Esmaeilpour, M Selzer, W Wenzel, H Stein, ... Advanced Energy Materials 13 (40), 2301985, 2023 | 4 | 2023 |