[Esip-machinelearning] Correct Meeting Link! Karthik Kashinath (NERSC/Lawrence Berkeley Lab) @ ESIP ML Cluster Telecon (3/19)

[Cindy Lin]

Dear Cluster/Committee Participants,

Here is the Youtube video recording
<https://www.youtube.com/watch?v=B_4TONeY75U> of Karthik Kashinath's
wonderful talk last Friday!

For any questions regarding the talk, you may send queries to Karthik on
karthikkashinath at gmail.com.

Best,
Cindy

On Mon, Mar 15, 2021 at 10:35 AM Cindy Lin <cindylky at umich.edu> wrote:

> Dear Cluster/Committee Participants,
>
>
> Big apologies for the previous email invite to our ML Cluster meeting*.
> Please note that the meeting is happening on Zoom rather than GoToMeeting
> this week. *The correct meeting link is pasted below.
>
>
> ***
>
>
> This is a gentle reminder that the next meeting
> for ESIP's Machine Learning Cluster is happening *this Friday, March 19,
> 12 pm ET/9 am PT*.
>
>
> For this week's cluster meeting, we are honored to have Karthik Kashinath
> from Lawrence Berkeley Lab give a talk.
>
>
> *Where: *
> https://us02web.zoom.us/j/88357173454?pwd=djkvOEx0TTZRekJLWVRHcEMyUitNZz09
> <https://www.google.com/url?q=https://us02web.zoom.us/j/88357173454?pwd%3DdjkvOEx0TTZRekJLWVRHcEMyUitNZz09&sa=D&source=calendar&ust=1616250140112000&usg=AOvVaw0wt7NE3Kv_dotHZTqVIUie>
>
>
> *Title: *Physics-informed Machine learning for weather and climate science
>
>
> *Abstract: *Machine learning (ML) provides novel and powerful ways of
> accurately and efficiently recognizing complex patterns, emulating
> nonlinear dynamics, and predicting the spatio-temporal evolution of weather
> and climate processes. ML and DL have had some remarkable successes in
> challenging problems in complex physical systems such as turbulent flows
> and weather and climate systems.
>
> However, off-the-shelf ML and DL models do not always obey the fundamental
> governing laws of physical systems, nor do they generalize well to
> scenarios on which they have not been trained. We discuss briefly
> approaches to incorporating physics and domain knowledge into ML models
> towards achieving greater physical consistency, reduced training time,
> improved data efficiency, and better generalization. Finally, we synthesize
> the lessons learned and identify scientific, diagnostic, computational, and
> resource challenges for developing truly robust and reliable
> physics-informed ML models for turbulence, weather, and climate processes.
>
>
> *Bio: *Karthik Kashinath leads various climate informatics projects at
> the Big Data Center @ NERSC (Lawrence Berkeley Lab). He received his
> Bachelors from the Indian Institute of Technology, Madras, Masters from
> Stanford University and PhD from the University of Cambridge, U. K. His
> background is in engineering and applied physics. He has worked on various
> projects spanning a wide range of disciplines from supersonic aircraft
> engines to battery technologies to complex chaotic systems and turbulence.
> His current research interests lie in physics-informed machine learning and
> novel data analytics and pattern discovery methods for large complex
> systems such as Earth’s climate. When he is not in front of the computer he
> runs up mountains, swims in lakes, and cooks exotic global cuisines.
>
>
> Meeting Agenda doc:
> https://docs.google.com/document/d/1NrnKP3KUBwKYDyVTXrTVBYg_yH3FmYbzsEiSQ6De5Ro/edit?usp=sharing
>
>
> Best,
>
> --
> *Cindy Lin*
> phd candidate | school of information + STS
> university of michigan, ann arbor
> cindylin.org
>


-- 
*Cindy Lin*
phd candidate | school of information + STS
university of michigan, ann arbor
cindylin.org
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