| Bayesian deep learning for partial differential equation parameter discovery with sparse and noisy data C Bonneville, C Earls Journal of Computational Physics: X 16, 100115, 2022 | 12 | 2022 |
| Gplasdi: Gaussian process-based interpretable latent space dynamics identification through deep autoencoder C Bonneville, Y Choi, D Ghosh, JL Belof Computer Methods in Applied Mechanics and Engineering 418, 116535, 2024 | 9 | 2024 |
| A principled approach to design using high fidelity fluid-structure interaction simulations W Wu, C Bonneville, C Earls Finite Elements in Analysis and Design 194, 103562, 2021 | 8 | 2021 |
| Gaussian processes for shock test emulation C Bonneville, M Jenquin, J Londono, A Kelly, J Cipolla, C Earls Reliability Engineering & System Safety 212, 107624, 2021 | 2 | 2021 |
| A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling C Bonneville, X He, A Tran, JS Park, W Fries, DA Messenger, SW Cheung, ... arXiv preprint arXiv:2403.10748, 2024 | | 2024 |
| Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical Simulations C Bonneville, Y Choi, D Ghosh, JL Belof NeurIPS 2023, ML and the Physical Sciences Workshop, 2023 | | 2023 |
| Bayesian Machine Learning Algorithms for Uncertainty Quantification, Optimization, and Equation Discoveries in Engineering Physics C Bonneville Cornell University, 2023 | | 2023 |
| Certified and parameterized latent space dynamics identification for time dependent image data C Bonneville, X He, JS Chen, W Fries, SW Cheung, D Ghosh, J Belof, ... | | |
Christopher EarlsProfessor of Engineering, Applied Mathematics, Theoretical and Applied MechanicsVerified email at cornell.edu
