PythonMHD: a new simulation code for astrophysical magnetohydrodynamics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PythonMHD is a new software package for astrophysical magnetohydrodynamic (MHD) simulations. Although it is a widely understood programming language in the physical sciences, Python has never previously been used to develop a comprehensive, research-oriented MHD simulation code. All of the existing MHD simulation codes are written in lower-level languages, such as C, C++, and FORTRAN. These programming languages are difficult to interpret and, thereby, exacerbate the learning curves associated with MHD software packages. The existing simulation codes further complicate the user’s experience by requiring separate software for data visualization and analysis. PythonMHD provides built-in tools for visualizing and analyzing simulation data while a simulation is still in progress, allowing the user to continuously monitor the evolution of their simulated system. In order to further reduce the likelihood of wasting the user’s time and computational resources on unproductive simulations, PythonMHD performs automatic error checking to assess whether the user’s simulation parameters and initial conditions are likely to generate numerical instabilities. This thesis describes the algorithms that are implemented in PythonMHD and documents their performance on standard 1D, 2D, and 3D MHD test problems. After using these test problems to demonstrate the accuracy of PythonMHD, this document presents a PythonMHD interstellar medium (ISM) turbulence generator, which is currently being used for novel research on astrophysical plasma lensing. In addition to the research applications of PythonMHD, this document explores the educational applications of PythonMHD by discussing how it has served as a teaching tool in a fourth year computational physics course (PHYS 4250) at the University of Manitoba.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it