Neural Network Reconstruction of Plasma Space-Time

We explore the use of Physics-Informed Neural Networks (PINNs) for reconstructing full magnetohydrodynamic solutions from partial samples, mimicking the recreation of space-time environments around spacecraft observations. We use one-dimensional magneto- and hydrodynamic benchmarks, namely the Sod, Ryu-Jones, and Brio-Wu shock tubes, to obtain the plasma state variables along linear trajectories in space-time. These simulated spacecraft measurements are used as constraining boundary data for a PINN which incorporates the full set of one-dimensional (magneto) hydrodynamics equations in its loss function. We find that the PINN is able to reconstruct the full 1D solution of these shock tubes even in the presence of Gaussian noise. However, our chosen PINN transformer architecture does not appear to scale well to higher dimensions. Nonetheless, PINNs in general could turn out to be a promising mechanism for reconstructing simple magnetic structures and dynamics from satellite observations in geospace.

THE DYNAMIC CALIBRATION UNCERTAINTY OF FAST RESPONSE PRESSURE PROBES

In this paper, error sources affecting the dynamic calibration of fast response pressure probes in shock tubes are examined. In particular, the sensors uncertainty, the uncertainty in the rising point of the pressure step and the nonideality of the step are treated. The latter refers to the presence of pressure oscillations past the shock front, which are particularly important in the case of low-pressure shock tubes, typically used for the calibration of pressure probes for turbomachinery applications. The nonideality effect is investigated using a Linear Time Invariant (LTI) second order model for the transfer function of the probe's line-cavity system and an existing analytical model for the post-shock oscillations. The effect of these uncertainty sources to the experimentally determined transfer function of a fast response probe calibrated in the von Karman Institute (VKI) shock tube are finally presented.