Wright State University recently issued the following announcement.
Physics Seminar: Multiscale, Nonlinear Space Physics ‘In the Wild’: From Fundamental Physics to Quantifying Risk
Thursday, March 31, 2022, 1 pm to 2 pm
Campus:
Dayton
Virtual
Audience:
Current Students
Faculty
Event Webpage:
Join virtual event
The speaker at the Physics seminar this week will be Dr. Sandra Chapman (pre-recorded). The talk was originally presented in 2020 when Dr. Chapman was awarded the 2020 AGU Lorenz Lecture prize.
Title: Multiscale, Nonlinear Space Physics ‘In the Wild’: From Fundamental Physics to Quantifying Risk
Abstract:
Solar system plasmas offer a rich laboratory for the fundamental physics of systems that are driven, dissipating and far from equilibrium. The sun, solar wind and earth’s magnetosphere exhibit non-linear processes that are coupled across a broad range of space and timescales resulting in bursty energy and momentum transport. A wealth of in-situ and remote observations are available from the fastest physical timescales of interest to across multiple solar cycles. There are significant challenges in deploying nonlinear physics and complex systems concepts ‘in the wild’ which are present across the geosciences. However, despite the fact that the behaviour of interest is typically non-time stationary, dominated by correlated extremes and often only available for a single realization, significant progress has been possible. Highlights include establishing the multi-scale nature of magnetospheric dynamics, unravelling the underlying physics of turbulence in the solar wind, and quantifying the risk of extreme space weather events and how it varies within and across the variable solar cycle. At its core, any analysis of observed systems, rather than controlled experiments, requires establishing robust, reproducible patterns and laws from multipoint data in these inhomogeneously sampled, non time stationary systems. There has been recent success with dynamical networks and machine learning is becoming a hot topic. If we can synthesize human thinking and machine learning, there is significant potential for progress given the wealth of data that is becoming available.
Original source can be found here.
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