CEDAR email: Invitation to submit an abstract to AGU session NG011 "Physics Informed Machine Learning in Nonlinear Geophysical Systems"

[kike]

Dear colleagues,

We welcome all researchers intrigued by the captivating intersection of
geophysical flows and machine learning to join our AGU session, titled
"NG011. Physics Informed Machine Learning in Nonlinear Geophysical Systems.”

https://agu.confex.com/agu/fm23/prelim.cgi/Session/190509

Your expertise and insights are invaluable as we explore the dynamic
relationship between non-linear geophysical systems and machine learning.
Whether you possess a fresh perspective, a groundbreaking approach, or an
intriguing discovery, your contribution will play a crucial role in
advancing our understanding of these interconnected fields.

Our session aims to address the challenges of modeling, predicting, and
comprehending nonlinear systems, especially in the presence of noisy
observational data. Physics Informed Machine Learning (PIML) has emerged as
a promising methodology to tackle these complex and high-dimensional
problems by integrating the principles of physics with observational data.

We invite you to submit your contributions that focus on the implementation
of PIML within nonlinear geophysical frameworks. This includes neutral and
ionized atmospheric layers, oceans, and tectonics, from various
observational, modeling, and theoretical perspectives. By embracing an
approach at the intersection of physics and machine learning, we seek to
gain fresh insights into the intricate behavior of complex geophysical
systems.

Submissions covering a broad range of PIML methods are encouraged,
including, but not limited to, regression techniques, neural networks, and
PDE-constrained inverse problems. We eagerly anticipate receiving your
research that pushes the boundaries of knowledge and opens new avenues of
exploration.

Join us at our  AGU session to engage in stimulating discussions, forge
connections with fellow researchers, and collectively expand the frontiers
of knowledge in the exciting realm where geophysical flows and machine
learning converge.

The session conveners,

Miguel Urco, Leibniz Institute of Atmospheric Physics
Koki Chau, Leibniz Institute of Atmospheric Physics
Kike Rojas Villalba, Cornell University
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