Publications

2023

Huang, Y., Greenberg, D. Symmetry Constraints Enhance Long-term Stability and Accuracy in Unsupervised Learning of Geophysical Fluid Flows. ESS Open Archive [preprint].

Arnold, C., Sharma, S., Weigel, T., Greenberg, D. :Efficient and Stable Coupling of the SuperdropNet Deep Learning-based Cloud Microphysics (v0. 1.0) to the ICON Climate and Weather Model (v2. 6.5). EGUsphere [preprint].

Schanz, T., Möller, K., Rühl, S., Greenberg, D. Robust detection of marine life with label-free image feature learning and probability calibration. Machine Learning: Science and Technology.

Rubbens, P., et al. Machine learning in marine ecology: an overview of techniques and applications. ICES Journal of Marine Science..

2022

Ramesh, P., Lueckmann, J.-M., Boelts, J., Tejero-Cantero, A., Greenberg, D.S., Gonçalves, P.J., & Macke, J.H. GATSBI: Generative Adversarial Training for Simulation-Based Inference. International Conference on Learning Representations.

2021

Nonnenmacher, Marcel, and David S. Greenberg. Learning Implicit PDE Integration with Linear Implicit Layers. The Symbiosis of Deep Learning and Differential Equations, Conference on Neural Information Processing Systems.

Nonnenmacher, M., & Greenberg, D.S. Deep emulators for differentiation, forecasting, and parametrization in Earth science simulators. Journal of Advances in Modeling Earth Systems.

Lueckmann, Jan-Matthis, Boelts, Jan, Greenberg, David, Goncalves, Pedro, Macke, Jakob. Benchmarking Simulation-Based Inference. International Conference on Artificial Intelligence and Statistics.

Paasche, Hendrik, Gross, Matthias, Lüttgau, Jakob, Greenberg, David, Weigel. To the brave scientists: Aren’t we strong enough to stand (and profit from) uncertainty in Earth system measurement and modelling? Geoscience Data Journal.

2020

Tejero-Cantero, A., Boelts, J., Deistler, M., Lueckmann, J., Durkan, C., Goncalves, P., Greenberg, D., Macke, J. sbi: A toolkit for simulation-based inference. The Journal of Open Source Software.

Gonçalves, P. J., Lueckmann, J. M., Deistler, M., Nonnenmacher, M., Öcal, K., Bassetto, G., Chintaluri, C., Podlaski, W. F., Haddad, S. A., Vogels, T. P., Greenberg, D. S., & Macke, J. H. (2020). Training deep neural density estimators to identify mechanistic models of neural dynamics. eLife.