Material for Collaboration Between Sai Ravela, ESSG, ERL, MIT and Dirk Smit, Shell Corporation
Areas of Collaboration
- Dynamic Super-resolution
- Anomaly Detection/Characterizaion of Carbonates
- Climate (?)
References to be used with Presentation.
Silva, S. James, C. L. Heald, S. Ravela, I. Mammarella, and W. J Munger, “A Deep Learning Parameterization for Ozone Dry Deposition Velocities“, Geophysical Research Letters, 2019
Blasch, E., S. Ravela, and A. Aved, “DDDAS: The Way Forward“, Handbook of Dynamic Data Driven Applications Systems: Springer, Cham, pp. 723–731, 2018
Ravela, S., “Tractable Non-Gaussian Representations in Dynamic Data Driven Coherent Fluid Mapping“, Handbook of Dynamic Data Driven Applications Systems: Springer, Cham, pp. 29–46, 2018
Gil, Y., S. A. Pierce, H. Babaie, A. Banerjee, K. Borne, G. Bust, M. Cheatham, I. Ebert-Uphoff, C. Gomes, M. Hill, et al., “Intelligent systems for geosciences: an essential research agenda“, Communications of the ACM, vol. 62, pp. 76–84, 2018.
Karpatne, A., I. Ebert-Uphoff, S. Ravela, H. Ali Babaie, and V. Kumar, “Machine learning for the geosciences: Challenges and opportunities“, IEEE Transactions on Knowledge and Data Engineering, 2018.
Ravela, S., “A Symbiotic Framework for coupling Machine Learning and Geosciences in Prediction and Predictability“, AGU Fall Meeting Abstracts, 2017
Ravela, S.., “Dynamic Data Driven Deep Learning“, DDDAS 17, Cambridge, MA, 08/2017
Ravela, S., “Dynamically Deformable Resampled Random Manifolds for High-dimensional, Nonlinear Inference in Geoscience in the presence of Uncertainty“, AGU Fall Meeting Abstracts, 2016
Ravela, S.., “A Non-Parametric Framework for Inference Using Dynamically Deformed and Targeted Manifolds“, SIAM AN 16, 07/2016
Ravela, S., and A. Sandu, “Dynamic Data-Driven Environmental Systems Science“, LNCS, vol. 8964: Springer, 2015
Ravela, S., “Statistical Inference for Coherent Fluids“, Dynamic Data-Driven Environmental Systems Science: Springer International Publishing, pp. 121–133, 2015
Ravela, S., “Learning and Information Approaches for Inference in Dynamic Data-Driven Geophysical Applications“, AGU Fall Meeting Abstracts, 2015.
Duyck, J., C. Finn, A. Hutcheon, P. Vera, J. Salas, and S. Ravela, “Sloop: A pattern retrieval engine for individual animal identification“, Pattern Recognition, vol. 48, no. 4: Elsevier, pp. 1059–1073, 2015.
Ravela, S., “Dynamic Data-driven Deformable Reduced Models for Coherent Fluids“, Procedia Computer Science, vol. 51, pp. 2464 – 2473, 2015.
Ravela, S., Generating a forecast by field coalescence, , 2015.
Tagade, P., and S. Ravela, “On a quadratic information measure for data assimilation“, American Control Conference (ACC), 2014: IEEE, pp. 598–603, 2014
Ravela, S., “Spatial inference for coherent geophysical fluids by appearance and geometry“, Applications of Computer Vision (WACV), 2014 IEEE Winter Conference on: IEEE, pp. 925–932, 2014.
Ravela, S., “Quantifying Uncertainty for Coherent Structures“, Procedia Computer Science, vol. 9: Elsevier, pp. 1187–1196, 2012
Belden, J., S. Ravela, T. T. Truscott, and A. H. Techet, “Three-dimensional bubble field resolution using synthetic aperture imaging: application to a plunging jet“, Experiments in Fluids: Springer, pp. 1–23, 2012.
Seto, C. J.., and S.. Ravela, “Differential Image Analysis to Extract Subsurface Flow Dynamics From High Resolution Surface Deformation Measurements“, AGU Fall Meeting Abstracts, pp. K7, 2010.
Yang, C. M., and S. Ravela, “Deformation invariant image matching by spectrally controlled diffeomorphic alignment“, Computer Vision, 2009 IEEE 12th International Conference on: IEEE, pp. 1303–1310, 2009.
Ravela, S., K. Emanuel, and D. McLaughlin, “Data assimilation by field alignment“, Physica D: Nonlinear Phenomena, vol. 230, no. 1: Elsevier, pp. 127–145, 2007
Ravela, S., A. Torralba, and WT. Freeman, “An ensemble prior of image structure for cross-modal inference“, Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, vol. 1: IEEE, pp. 871–876, 2005.