| Solving Allen-Cahn and Cahn-Hilliard equations using the adaptive physics informed neural networks CL Wight, J Zhao arXiv preprint arXiv:2007.04542, 2020 | 206 | 2020 |
| Constrained block nonlinear neural dynamical models E Skomski, S Vasisht, C Wight, A Tuor, J Drgoňa, D Vrabie 2021 American Control Conference (ACC), 3993-4000, 2021 | 17 | 2021 |
| Differential property prediction: a machine learning approach to experimental design in advanced manufacturing L Truong, WJ Choi, C Wight, E Coda, T Emerson, K Kappagantula, ... TMS Annual Meeting & Exhibition, 587-595, 2023 | 1 | 2023 |
| Fiber bundle morphisms as a framework for modeling many-to-many maps E Coda, N Courts, C Wight, L Truong, WJ Choi, C Godfrey, T Emerson, ... Topological, Algebraic and Geometric Learning Workshops 2022, 79-85, 2022 | 1 | 2022 |
| Numerical Approximations of Phase Field Equations with Physics Informed Neural Networks C Wight | 1 | 2020 |
| Decomposing the hamiltonian of quantum circuits using machine learning J Burns, Y Sung, C Wight | 1 | 2019 |
| A Topological-Framework to Improve Analysis of Machine Learning Model Performance H Kvinge, C Wight, S Akers, S Howland, W Choi, X Ma, L Gosink, E Jurrus, ... arXiv preprint arXiv:2107.04714, 2021 | | 2021 |
| Dataset to Dataspace: A Topological-Framework to Improve Analysis of Machine Learning Model Performance H Kvinge, C Wight, S Akers, S Howland, W Choi, X Ma, L Gosink, E Jurrus, ... | | |
