2021
Dandekar, R., Chung, K., Dixit, V., Tarek, M., Garcia-Valadez, A., Vemula, K. V., and Rackauckas, C. (2020). Bayesian Neural Ordinary Differential Equations. ArXiv. https://arxiv.org/abs/2012.07244
Kim, S., Ji, W., Deng, S., Ma, Y., and Rackauckas, C. (2021). Stiff neural ordinary differential equations. Chaos: An Interdisciplinary Journal of Nonlinear Science, 31(9), 093122. https://doi.org/10.1063/5.0060697
Loose, N., & Heimbach, P. (2021). Leveraging Uncertainty Quantification to Design Ocean Climate Observing Systems. Journal of Advances in Modeling Earth Systems, 13(4), 1-25. doi:10.1029/2020ms002386
Morlighem, M., Goldberg, D., Santos, T. D. dos, Lee, J., and Sagebaum, M. (2021). Mapping the Sensitivity of the Amundsen Sea Embayment to Changes in External Forcings Using Automatic Differentiation. Geophysical Research Letters, 48(23). https://doi.org/10.1029/2021gl095440
Moses, W. S., Churavy, V., Paehler, L., Hueckelheim, J., Narayanan, S. H. K., Schanen, M., & Doerfert, J. (2021). Reverse-Mode Automatic Differentiation and Optimization of GPU Kernels via Enzyme. Proceedings of SC’21, November 14-19, 2021, https://doi.org/10.1145/3458817.3476165
Pal, A., Ma, Y., Shah, V., and Rackauckas, C. (2021). Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics. Proceedings of Machine Learning Research, 139, 8325–8335. https://proceedings.mlr.press/v139/pal21a.html
Pestourie, R., Mroueh, Y., Rackauckas, C., Das, P., and Johnson, S. G. (2021). Physics-enhanced deep surrogates for PDEs. ArXiv. https://arxiv.org/abs/2111.05841
Rackauckas, C., Anantharaman, R., Edelman, A., Gowda, S., Gwozdz, M., Jain, A., Laughman, C., Ma, Y., Martinuzzi, F., Pal, A., Rajput, U., Saba, E., & Shah, V. B. (2021). Composing Modeling and Simulation with Machine Learning in Julia. Proceedings of 14th Modelica Conference 2021. https://arxiv.org/abs/2105.05946
Roesch, E., Rackauckas, C., and Stumpf, M. P. H. (2021). Collocation based training of neural ordinary differential equations. Statistical Applications in Genetics and Molecular Biology, 20(2), 37–49. https://doi.org/10.1515/sagmb-2020-0025
Schaefer, F., Tarek, M., White, L., & Rackauckas, C. (2021). AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia. NeurIPS 2021 Differentiable Programming Workshop. https://arxiv.org/abs/2109.12449.
Willcox, K. E., Ghattas, O., & Heimbach, P. (2021). The imperative of physics-based modeling and inverse theory in computational science. Nature Computational Science, 1(3), 166-168. doi:10.1038/s43588-021-00040-z