NG003
Applied Math Perspectives on Modeling, Analyzing, and Predicting Complex Nonlinear Geophysical Systems
Session ID#: 253669
Session Description:
Nonlinear phenomena in complex multiscale turbulent dynamic systems are ubiquitous in geoscience. Effective modeling methods and efficient computational analysis in these geophysical processes remain a significant challenge in contemporary science, with substantial social implications for pressing issues such as climate change. Modeling and forecasting these complex systems are especially challenging due to the intermittent energy transfer between unresolved subscales induced by nonlinear effects and the occurrence of extreme events. Therefore, it is of practical importance to develop novel stochastic models, design new algorithms for analyzing and predicting these systems, and implement new model-based and machine-learning techniques to advance efficient forecasts. This session aims to integrate novel applied math tools with geophysical systems. The main themes will include, but not be limited to, stochastic and statistical modeling, machine learning theory and algorithms, understanding intermittency, predicting rare and extreme events, data-driven algorithms, multiscale analysis, data assimilation, and uncertainty quantification.
Co-Sponsor(s):
Index Terms:
1942 Machine learning [INFORMATICS]
1990 Uncertainty [INFORMATICS]
3315 Data assimilation [ATMOSPHERIC PROCESSES]
4468 Probability distributions, heavy and fat-tailed [NONLINEAR GEOPHYSICS]
Primary Convener: Nan Chen, University of Wisconsin-Madison, Department of Mathematics, Madison, United States
Convener: Di Qi, Purdue University, Mathematics, West Lafayette, IN, United States
The Ensemble Kalman Inversion Race (1862970)
Rebecca Gjini, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, United States, Matthias Morzfeld, Scripps Institution of Oceanography, University of California, San Diego, Institute of Geophysics and Planetary Physics, La Jolla, United States, Oliver Dunbar, California Institute of Technology, Pasadena, CA, United States and Tapio Schneider, California Institute of Technology, Environmental Science and Engineering, Pasadena, United States
A Mathematical Framework for Quantifying Nonlinear Uncertainty Propagation in Eddy Identification Criteria (1874010)
Charlotte Moser, University of Wisconsin Madison, Madison, United States, Nan Chen, University of Wisconsin-Madison, Department of Mathematics, Madison, United States and Stephen Wiggins, United States Naval Academy, Mathematics, Naval Academy, United States
A nonlinear full-field conceptual model for ENSO diversity (1882475)
Xianghui Fang1, Henk Dijkstra2, Claudia Wieners3 and Francesco Guardamagna3, (1)Fudan University, Shanghai, China, (2)Utrecht University, Institute for Marine and Atmospheric research, Utrecht, Netherlands, (3)Utrecht University, Utrecht, Netherlands
A Physics-Informed Auto-Learning Framework under Partial Observations, with Applications to Developing Stochastic Conceptual Models (1884522)
Yinling Zhang, University of Wisconsin Madison, Madison, WI, United States, Nan Chen, University of Wisconsin-Madison, Department of Mathematics, Madison, United States, Xianghui Fang, Fudan University, Department of Atmospheric and Oceanic Sciences, and Institute of Atmospheric Sciences, Shanghai, China and Jérome Vialard, LOCEAN-IPSL, CNRS-IRD-MNHN-Sorbonne Université, Paris, France
Assimilative Causal Inference (1887664)
Nan Chen, University of Wisconsin Madison, Madison, WI, United States, Marios Andreou, University of Wisconsin-Madison, Department of Mathematics, Madison, United States and Erik Bollt, Clarkson University, Department of Electrical & Computer Engineering, Potsdam, United States
What do we learn from looking at the Hasselmann model through two lenses: Stochastics meets statistics. (1890625)
Nicholas Wynn Watkins, University of Warwick, CFSA, Physics, Coventry, United Kingdom; London School of Economics, London, United Kingdom and David Alan Stainforth, London School of Economics, Grantham Research Institute, London, United Kingdom
A stochastic precipitating quasi-geostrophic model (1898840)
Changhong Mou, Utah State University, Department of Mathematics and Statistics, Logan, United States, Nan Chen, University of Wisconsin-Madison, Department of Mathematics, Madison, United States, Leslie M Smith, University of Wisconsin Madison, Madison, United States and Yeyu Zhang, Shanghai University of Finance and Economics, School of Mathematics, Shanghai, China
Reduced Order Modeling of Multi-Scale Geoscientific Data with Shallow Recurrent Decoder Networks (1899763)
Hiroto Bito1, Marine Denolle1, Yiyu Ni1, Claire Jensen1, Mars Gao2, Jonathan R. Bedford3, Elvira Latypova3 and Nathan Kutz4, (1)University of Washington Seattle Campus, Department of Earth and Space Sciences, Seattle, United States, (2)University of Washington Seattle Campus, Paul G. Allen School of Computer Science & Engineering, Seattle, United States, (3)Ruhr University Bochum, Institute of Geosciences, Bochum, Germany, (4)University of Washington Seattle Campus, Departments of Applied Mathematics and Electrical & Computer Engineering, Seattle, United States
Extreme Event Aware Learning (1926244)
Kai Chang, Massachusetts Institute of Technology, Center for Computational Science and Engineering, Cambridge, MA, United States and Themistoklis Sapsis, Massachusetts Institute of Technology, Cambridge, United States
Stochastic modeling of coupled climate variables and ice volume over the Pleistocene glacial cycles (1928067)
Pijush Patra, NORDITA, Stockholm, Sweden, Ludovico Theo Giorgini, Massachusetts Institute of Technology, Cambridge, United States and John S. Wettlaufer, Yale University, New Haven, CT, United States
Towards Dynamically Debiased andSuper-Resolved Emulators for Extreme EventPrediction in Climate Systems (1946704)
Stamatios Stamatelopoulos1, Zhong Yi Wan2, Ignacio Lopez-Gomez2, Robert Carver2, Leonardo Zepeda-Núñez2, Fei Sha2 and Themistoklis Sapsis3, (1)Massachusetts Institute of Technology, Cambridge, MA, United States, (2)Google Research, Mountain View, United States, (3)Massachusetts Institute of Technology, Cambridge, United States
Non‐asymptotic error bounds for nonlinear and linear ensemble transport filters (1977982)
Frederic Jorgensen1, Youssef Marzouk2, Ricardo Baptista3 and Franca Hoffmann3, (1)Massachusetts Institute of Technology, Cambridge, MA, United States, (2)Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, Cambridge, United States, (3)California Institute of Technology, Pasadena, United States
