The mathema cs of Space Weather Forecas ng using Ar ficial Intelligence
Contact person: Giovanni Lapenta, CPA.
In the past ten years the field of Machine Learning has rekindled the excitement on Ar ficial Intelligence,
to the point that leaders in science and industry consider it the top challenge of the modern world [1]. We
are on the brink of making again new discoveries in science with the help of computers. Students who
graduate this year in the fields of mathema cs and physics will be confronted in the near future with the
use of intelligent systems trained using Machine Learning, whether they join the private sector or pursue
a carrier in science. This fast growing domain of technology is already crea ng jobs and services that will
drive the economy in the next ten years.
Space Weather is a perfect target for the use of Machine Learning. With the current computa onal
power it is impossible to perform a first principle simula on of the en re heliosphere and even if it were
the data available is insufficient to constrain the models. Instead, Machine Learning can be used to perform
forecas ng of the solar wind condi ons or to help discover new scien fic features previously unknown.
a)
b)
c)
Figure 1: SDO images of the sun at two wavelenghts (panels a,b), and magnetogram (panel c).
In this project, the student will study the mathema cal methods behind the Machine Learning algorithms used to automa cally detect correla ons between observa ons of the solar disk, and in-situ measurements of the solar wind condi ons at the L1 orbit. Figure 1 shows three images of the Sun taken by the
SDO satellite on November 2016. These images will be used as inputs to the Machine Learning algorithm
used by the student.
The student will:
1. learn the mathema cal methods used in deep neural networks;
2. learn how to create a convolu onal neural network using mathema cal methods (matrices and gradient descent itera ons);
3. download observa ons of the sun and the solar wind from the SDO and the DSCOVR missions;
4. train the network to perform forecas ng of the solar wind condi ons are the L1 orbit, including solar
wind speed, density, IMF clock angle and temperature.
5. perform comparisons against in-situ data, as shown in Figure 2.
Figure 2: In-situ measurements of the solar wind at the L1 orbit
The final goal of the project is to perform an analysis of the possible combina ons of inputs and outputs
that lead to the best forecas ng results. The student will develop a simple forecas ng model designing the
mathema cal methods and implemen ng them in a small program using Machine Learning algorithms. By
the end of the project he/she will be able to:
• understand neural networks, their mathema cal methods and their meaning,
• have a clear understanding of the connec ons between the Sun and the Earth,
• create a Machine Learning system for any other applica on in science but also in any of the vast
domains of applica on of AI.
This is a very new field of research and all the developments made by the student can be published in
high impact journals. Students can take inspira on from the links below.
References
[1] http://observer.com/2015/08/stephen-hawking-elon-musk-and-bill-gates-warn-about-artificial-intelligence/
[2] Computers learning to play Atari games: https://www.youtube.com/watch?v=rbsqaJwpu6A
[3] How to use TensorFlow to recognize handwri en numbers: https://www.youtube.com/watch?v=qyvlt7kiQoI
[4] Learning from Big Data for Physics: https://www.youtube.com/watch?v=Vyq2r8bEYoA
[5] A Deep-Learning Approach for Opera on of an Automated Real me Flare Forecast, Yuko Hada-Muranushi, Takayuki Muranushi, Ayumi Asai, Daisuke Okanohara, Rudy Raymond, Gentaro
Watanabe, Shigeru Nemoto, Kazunari Shibata, arXiv:1606.01587.
[6] Solar Flare Predic on Model with Three Machine-Learning Algorithms Using Ultraviolet Brightening and Vector Magnetogram, N. Nishizuka, K. Sugiura, Y. Kubo, M. Den, S. Watari, M. Ishii,
arXiv:1611.01791.
[7] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. Deep learning, Nature 521.7553 (2015): 436-444.
[8] Why does deep and cheap learning work so well?, Henry W. Lin and Max Tegmark, arXiv:1608.08225v2.
Page 2