Inaccuracies arise from round off errors, data type conversion and many other sources introduced in the large scale multi computational platform models of today.

Data provided to a machine learning model may not cover the full extent of the problem, this is especially important for BVP, and problems with long range order.

Lack of sufficient random variables to produce higher quality pseudorandom number generation. Poor random number generation can lead to un physical results in data. [Dongjie Zhu et al J. Stat. Mech. (2023) 073203]

Training data maters, order matters. By choosing data at random can lead to bias in various sections of a models output.

Stored memory in a neural network may lead to poor performance or hallucinations, and needs further exploration.

Different computers have limited addressing and in todays interconnected cloud, fog and IOT based compute platforms open the question of how well API software maintains floating point accuracy

*Work in progress *

work in progress