
This educational handout by Matthew J. Hall, created with assistance from GPT-5 Thinking, provides a clear, step-by-step introduction to the relationship between entropy, gravity, and the arrow of time.Written in plain language for students and general readers, it explains how microscopic dynamics and coarse-graining produce the Second Law of Thermodynamics, how gravity bends entropy toward clumping, and how black holes force us to include geometry in thermodynamic reasoning.Each section pairs mathematical expressions with plain-English explanations, showing how the arrow of time emerges from low-entropy initial conditions and evolves through statistical and geometric processes.This work forms part of Hall’s broader educational series on time, gravity, and dynamics, connecting thermodynamics, cosmology, and relativity. Keywords
relativity, educational physics, time physics, coarse-graining, Raychaudhuri equation, arrow of time, Bekenstein-Hawking entropy, black holes, Matthew J. Hall, gravity, thermodynamics, GPT-5 ThinkingGPT-5 Thinking, second law of thermodynamics, entropy, cosmology
relativity, educational physics, time physics, coarse-graining, Raychaudhuri equation, arrow of time, Bekenstein-Hawking entropy, black holes, Matthew J. Hall, gravity, thermodynamics, GPT-5 ThinkingGPT-5 Thinking, second law of thermodynamics, entropy, cosmology
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
