My theoretical work has produced emergent qualitative results, and I'm developing simulation software to generate quantitative proof and reconcilable statistics.
To test small-scale mechanics, I create scenarios with a small set of entities, and stop within a small number of events on the timeline. I verify timeline events within expected limits, in logs or visualisations in a web browser.
These randomize the vacuum, or some other preconditions, and run the simulation many times, to produce a data set of equivalent events, and a map of variant properties. Examples might include: Compton radius of a particle, field statistics, and functions for covariance of properties.
Where datasets are compared with criteria, I can describe more advanced phenomena. Examples include: probability of decoherence, confidence of escaping a black hole, matter/anti-matter polarization, and decay distributions.
With appropriate navigation controls, present the simulation state as an animated diagram that demonstrates phenomena, with emphasis on items of interest.
Real-world applications need high numerical precision to represent phase values of less than 10^-30 of Planck length, on the scale of classical distances. I expect to use a library like BigNum for this, at the expense of performance and inconvenient implementation.
To reconcile with standard physics, I'd generate statistical results with sufficient confidence. This needs statistical methods I've not developed, and also computing power and optimisations. I expet the first results will be qualitative and illustrative, followed by low-precision statistical results that I hope will converge on the desired outcome.
The simulation will be sub-optimal at macro scale, and is best suited to high energy scenarios. Because an environmental vacuum contains many overlapping spheres, a large space of low-energy entities presents combinatorial challenges that are not easily optimised solely by space-partitioning. Phase and mass partitioning might help to discard irrelevant combinations of entities.
The simulation has free parameters for example masses of entities. In the reconciled simulation, while I expect fewer free parameters than standard models, I want to replace them with values derived from geometric expressions.