Synopsis: (limit of 400 characters)Heliophysics depends on RSEs to properly engineer software.However, RSEs receive unequal treatment compared to their science counterparts, resulting in unsustainable talent loss.These restrictions include lack of credit for their contributions and insufficient training.This paper describes what a RSE is and proposes solutions, including implementing appropriate recognition standards.

The nnde package provides a pure-Python implementation of one of the earliest approaches to using neural networks to solve differential equations the trial function method (Lagaris et al., 1998). The nnde package was initially developed as a vehicle for understanding the internal workings of feedforward neural networks, without the constraints imposed by an existing neural network framework. It has since been enhanced to provide the capability to solve differential equations of scientific interest, such as the diffusion equation described here. The ultimate goal of the package is to provide the capability to solve systems of coupled partial differential equations, such as the equations of magnetohydrodynamics.

Software is crucial to all areas of modern plasma science research. Laboratory plasma physicists use software to interpret plasma diagnostics, analyze experimental results, and glean insights using advanced techniques from data science. Space scientists use software to reduce and understand in situ observations. Numericists use software to simulate the behavior of laboratory, heliospheric, and astrophysical plasmas, and then analyze or visualize the results. Theorists use symbolic manipulation software to perform or check derivations. Cross-disciplinary research and cross-comparisons between experiments, observations, simulations, and theories all require software. Despite our heavy reliance on software, funding agencies have traditionally had few avenues available to support development of general purpose research software infrastructure.

transplant2mongo allows users to transform Standard Transplant Analysis and Research (STAR) ASCII data files from the Organ Procurement and Transplantation Network (OPTN) into a MongoDB database [1, 2]. The STAR data are a complex collection of tab-separated files with inter-related records that are not amenable to complex queries. A researcher planning to use OPTN STAR data can use transplant2mongo to convert the data into a MongoDB database and then use open-source tool software for analysis. The source code for transplant2mongo is available on GitHub at https://github.com/ceharvs/transplant2mongo and includes sample data files for initial testing and queries. Funding Statement: The software referenced herein is copyright of The MITRE Corporation and the result of MITRE’s Early Career Research program and work done in the Computational Science and Informatics program at George Mason University. Approved for Public Release; Distribution Unlimited. Case Number 18-0298. The author’s affiliation with The MITRE Corporation is provided for identification purposes only and is not intended to convey or imply MITRE’s concurrence with, or support for, the positions, opinions, or viewpoints expressed by the author.

Magnetosphere dynamics driven by the solar wind are very complex, involving both coherent responses to the extremal loading and multiscale features characteristic of critical phenomena. Earlier attempts to explain their complexity in terms of dynamical chaos did not take into account the spatially extended nature of the system and multiscale coupling. A more consistent description can be made using cellular automata models. In particular, the hypothesis of the selforganized critical state of the magnetosphere, which is based on a certain class of cellular automata models, provides a physical basis for the observed power-law spectra of magnetospheric activity. However, this is not enough to explain other features of this activity such as the characteristic scales of storms and substorms and apparent dependence of these phenomena on the solar wind loading. The analysis of correlated sets of solar wind and auroral index data suggest a more general framework for modeling the magnetospheric activity. It reveals in particular both the multiscale processes resembling classical critical phenomena in phase transition physics and regular components of dynamics, which resemble first order phase transitions. Similar to classical critical phenomena, the multiscale properties of substorms depend on the solar wind parameters. Thus, the data-derived picture of substorms differs from the self-organized criticality. However, it is surprisingly consistent with a modem theory of critical phenomena based on cellular-automata with finite driving and dissipation rates, which considers self-organized criticality as a limiting regime of the special type of phase transitions in non-equilibrium systems. The new framework is shown also to provide efficient tools for predicting both global and multiscale features of magnetospheric activity.

The solar-wind-driven magnetosphere–ionosphere exhibits a variety of dynamical states including low-level steady plasma convection, episodic releases of geotail stored plasma energy into the ionospheric known broadly as substorms, and states of continuous strong unloading. The WINDMI model [J. P. Smith et al., J. Geophys. Res. 105, 12 983 (2000)] is a six-dimensional substorm model that uses a set of ordinary differential equations to describe the energy flow through the solar wind–magnetosphere–ionosphere system. This model has six major energy components, with conservation of energy and charge described by the coupling coefficients. The six-dimensional model is investigated by introducing reductions to derive a new minimal three-dimensional model for deterministic chaos. The reduced model is of the class of chaotic equations studied earlier [J. C. Sprott, Am. J. Phys. 68, 758 (2000)]. The bifurcation diagram remains similar, and the limited prediction time, which is in the range of three to five hours, occurs in the chaotic regime for both models. Determining all three Lyapunov exponents for the three-dimensional model allows one to determine the dimension of the chaotic attractor for the system.

The possibility of controling the pointing stability of a slowly pulsed Ti:Sapphire laser system by lowpass filters and artificial neural networks (NN) is investigated by performing time series analysis and computer simulations on experimentally measured datasets. The simulations show that at pulse repetition rates of 20 Hz it is possible to use a feedforward algorithm to reduce the angular standard deviation from 0.7 to 0.3 mu rad. The properties and advantages of NN methods such as automatic adaptation characteristics of a time series are discussed. (C) 2001 American Institute of Physics.

An optimization study of the prediction performance for the substorm model WINDMI is presented. The model is based on the Earth's magnetospheric dynamics and provides a low order description of the nightside energy loading and unloading that takes place during the substorm process. Previous studies of this model on isolated substorms have indicated that it can be a good predictor of solar wind driven substorm activity as measured by fluctuations in the AL index for selected substorms. Because the model is based on a set of VB s driven nonlinear ordinary differential equations which can exhibit bifurcation and catastrophe like behavior, an optimization of the model using conventional minimization techniques over a large data set does not work well. For such systems the genetic algorithm method of optimization is more efficient at exploring the parameter space. We present the results of a genetic algorithm optimization of WINDMI using the Blanchard-McPherron and the Bargatze data set and test statistically alternative forms of the model which include the effects of ionospheric conductivity enhancements and region 2 coupling. A key result from the large scale computations used to search for a uniform convergence of the prediction over the 117 substorm database, is the finding that there are three distinct types of VB s-AL wave forms characterizing the substorms in the Blanchard-McPherron database. Two types are given by the internally triggered WINDMI model and the third type requires an external trigger such as the northward turning of the IMF model of Lyons 1995.

Neural networks are developed for reconstructing the chaotic attractor in the nonlinear dynamics of the solar wind driven, coupled magnetosphere‐ionosphere (MI) system. Two new methods which improve predictive ability are considered: a gating method which accounts for different levels of activity and a preconditioning algorithm which allows the network to ignore very short time fluctuations during training. The two networks are constructed using the Bargatzeet al.[1985] substorm database that contains solar wind speed and interplanetary magnetic field (IMF) along with ionospheric electrojet index, AL. Both networks are found to produce improvements in predictability, and the significance of the performance increase of the gated network is demonstrated using the bootstrap model testing method.