Ensuring AI For Science is Science: Making Randomness Portable

Science is a practice of systematically studying something and offering data and evidence to reach a conclusion. With first principles simulations, basic physics are used to model some phenomena leading to consistent, repeatable results. With an incomplete physics model or models too complex or costly to run for a given task, AI or ML are being used to estimate what the missing physics would be if we could meet our goals with a first principles approach. Our work has been exploring how to ensure ML is capable of offering a science level of consistency so we can trust our science applications incorporating ML models. Our earlier work examined the impact of pseudorandom numbers on model quality. For this study, we have examined the pseudo-random number generation algorithms used to seed essentially all ML algorithms to ensure that model generation can be performed by other scientists to achieve identical results.