This project analyzes the historical development of general relativistic magnetohydrodynamics (GRMHD) simulations as a case study in scientific and technological progress. GRMHD has become a central tool in astrophysics, enabling modeling of black hole accretion, jets, and high-energy phenomena. However, the field’s progress has been uneven and driven by a combination of algorithmic innovation, computational scaling, and conceptual breakthroughs.
This project aims to systematically document and analyze these drivers of progress, producing a detailed account of how GRMHD simulations evolved and what factors most strongly influenced their advancement.
The final output will be a long-form research essay suitable for publication in a peer-reviewed journal as well as platforms such as LessWrong and arXiv, targeting both academic and meta-science audiences.
Goals:
Identify key inflection points in the development of GRMHD simulations
Quantify the relative importance of:
algorithmic improvements
increased computational power
theoretical insights
Understand bottlenecks that slowed progress
Extract general lessons about how computational science advances
Approach:
Literature review of GRMHD papers over the past ~20–30 years
Categorization of advances (e.g., numerical schemes, resolution, hardware scaling)
Timeline construction of major breakthroughs
Analysis of trends (e.g., performance vs. compute, method adoption)
Synthesis into a structured “progress model” for the field
My PhD program no longer has the funds to provide me a stipend, so this would serve in its place and allow me to complete this project at the intersection of physics and progress studies as the final piece of my dissertation.
I am nearly done with a first-author technical paper on original GRMHD simulations that will be submitted to ApJ.
Risks:
Difficulty obtaining consistent or comparable historical benchmarks
Overly qualitative conclusions if quantitative data is limited
Scope creep (field is broad and technically complex)
Mitigations:
Focus on well-documented milestones and widely used codes
Combine qualitative and quantitative analysis
Maintain a tightly scoped case study
Failure outcome:
A less rigorous or less generalizable analysis, but still a useful descriptive overview of the field’s development
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