Temporal reversibility of a fluid mixture under concentration gradient

This project is a specialized academic implementation accompanying a scientific paper on non-equilibrium thermodynamics. With 0 stars and 4 forks (likely the research team and immediate peers), it functions as a reference implementation for a specific physical hypothesis rather than a software product. Its defensibility is low from a commercial software perspective because it is a niche scientific tool, but it possesses high domain specificity. Frontier labs like OpenAI or Google DeepMind are unlikely to compete here directly, as the problem is highly specialized fluid physics, though DeepMind's broader 'AI for Science' initiatives could theoretically simulate such systems. There is no platform risk because this is research code, not a service. The displacement horizon is 'unlikely' because scientific code isn't typically 'displaced' by competitors in a market sense; it is either used for further research or superseded by newer physical models.