[ASA] 22nd and 23rd February 2023 : two talks in Solar physics and Machine Learning Apps for Space Weather in the School of Mathematics, Monash University,

[Alina Donea]

Dear all,

I would like to draw your attention to two talks in Solar physics and  Machine Learning  in the School of Mathematics, 
Monash University, the online zoom access is  included. 

Also, the second part of the workshop in deep learning applied to 
solar data and (medical) images is continuing on 23rd Feb.


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22/02/2023

11 am
Dr Istvan Ballai

Title: A new perspective on waves in partially ionised solar plasmas

Abstract: The temperature in the lower part of the solar atmosphere is not high enough to ensure a full ionisation of the plasma, meaning that at all time the plasma is made up from charged particles and neutrals that interact via short and long range collisions. These collisions provide an effective momentum and energy transfer mechanism between different species. 
In my presentation I will discuss some interesting  properties of waves in weakly ionised plasmas. The framework in which the properties of waves are described depends on the relative magnitude of collisions between particles, but also on the relative magnitude of the collisional frequencies compared to the gyro-frequency of charged particles. 
The second part of my presentation will deal with waves in partially ionised plasma in ionisation non-equilibrium that can arise due to to collisional impact ionization and radiative recombination. The process of non-equilibrium ionization is very much related to the process of irreversible physics via inelastic collision between particles. Here I will discuss the limitations of the analytical model and present some preliminary results related to a peculiar parametric instability that appears in partially ionised plasma environments in ionisation non-equilibrium.


https://protect-au.mimecast.com/s/q-KpC91WPRTmy9lY4ioOAHP?domain=monash.zoom.us

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23/02/2023
Dr Enrico Camporeale
Title: The state-of-the-art of machine learning applications for space weather problems 

In the last few years machine learning techniques have proven capable of forecasting space weather events with a much higher accuracy with respect to long-used traditional empirical and physics-based models. Even though very few operational models are currently empowered by machine learning, it appears to be unavoidable that the community will embrace in the near future such powerful techniques. Indeed, it is hard to imagine the future of space weather without machine learning. Presently, we are moving one step further from the initial 'early-adopter' stage, where proof-of-principles models were elaborated and tested, and more consideration is being given to the issues of reliability, uncertainty, and trustworthiness of machine learning models, finding the right balance between physics priors and data-driven discovery.
In this talk I will present the state-of-the-art of machine learning applications for space weather problems and will discuss a few challenges and opportunities that this field presents to us.

 the zoom link 
https://protect-au.mimecast.com/s/sF0rC0YKPvi2OZ7rNhDNbYf?domain=monash.zoom.us
Meeting ID:	827 8195 6026
Passcode:	795960

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Second Part of the Workshop:

https://protect-au.mimecast.com/s/7sG9CgZ0N1ilXpoYEc2F1R8?domain=monash.edu
====================

https://protect-au.mimecast.com/s/hVpeCjZ1N7ijLPByoI7HWA_?domain=youtube.com
Deep Learning For Space Research at MonashU
youtube.com



====
Alina Donea, Dr
Senior Lecturer in Solar Physics and Applied Mathematics
School of Mathematics, Monash University
phone:03 9905 4488
www: https://protect-au.mimecast.com/s/POYZCk81N9tnV1Kq2sJaHrR?domain=research.monash.edu
BSc and Diploma Physics, PhD Mathematics and Astronomy, GCHE Monash
Member of IAU, ASA, AIP-Solar Terrestrial and Space Physics , Solar Physics Advisory Board member
Associate Editor: Frontiers Astronomy and Space Sciences 
Editor of Earth and Space Science
Chair of the Space and Terrestrial and Solar Physics Group, Australian Institute of Physics

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