On the potential of recurrent neural networks for modeling path dependent plasticity MB Gorji, M Mozaffar, JN Heidenreich, J Cao, D Mohr Journal of the Mechanics and Physics of Solids 143, 103972, 2020 | 243 | 2020 |
Modeling structure-property relationships with convolutional neural networks: Yield surface prediction based on microstructure images JN Heidenreich, MB Gorji, D Mohr International Journal of Plasticity 163, 103506, 2023 | 47 | 2023 |
Modeling stress-strain curves with neural networks: a scalable alternative to the return mapping algorithm ML du Bos, F Balabdaoui, JN Heidenreich Computational Materials Science 178, 109629, 2020 | 24 | 2020 |
Design of isotropic porous plates for use in hierarchical plate-lattices JN Heidenreich, MB Gorji, T Tancogne-Dejean, D Mohr Materials & Design 212, 110218, 2021 | 20 | 2021 |
Transfer learning of recurrent neural network‐based plasticity models JN Heidenreich, C Bonatti, D Mohr International Journal for Numerical Methods in Engineering 125 (1), e7357, 2024 | 11 | 2024 |
Recurrent neural network plasticity models: Unveiling their common core through multi-task learning JN Heidenreich, D Mohr Computer Methods in Applied Mechanics and Engineering 426, 116991, 2024 | 6 | 2024 |
ExpLIMEable: A Visual Analytics Approach for Exploring LIME S Laguna, JN Heidenreich, J Sun, N Cetin, I Al-Hazwani, U Schlegel, ... 2023 Workshop on Visual Analytics in Healthcare (VAHC), 27-33, 2023 | 2 | 2023 |
Toward Neural Network Models to Model Multi-phase Solids MB Gorji, JN Heidenreich, M Mozaffar, D Mohr Forming the Future: Proceedings of the 13th International Conference on the …, 2021 | 1 | 2021 |
ExpLIMEable: An exploratory framework for LIME S Laguna, J Heidenreich, J Sun, N Cetin, I Al Hazwani, U Schlegel, ... XAI in Action: Past, Present, and Future Applications, 2023 | | 2023 |
On the potential of convolutional neural networks for estimating the structural response of two-material structures J Heidenreich Massachusetts Institute of Technology, 2020 | | 2020 |