Using artificial intelligence to predict and validate nuclear data

Nuclear data underpins all of nuclear science and technology. Even the most complex and concisely written nuclear data analysis tools are unreliable and untrustworthy if they use old or un-benchmarked nuclear data.
Nuclear data are comprised of cross sections, angular scattering probabilities, outgoing energy probabilities, reaction product multiplicities, fission yield data, reaction products and more. All of which are vitally important in the design and safety cases of nuclear devices.

- Predicting the functional form of (n,2n), thermal, scattering cross sections, with little or no measurements, based on learning (many papers could be written based on the same methodology)
- AI cross section lookup table based on learning cross section forms
- AI uncertainty analysis - learn to predict cross sections of well characterised cross sections within bounds of uncertainty. Use variation of synapse weights to create a cross section probability distribution function, which can be converted to a statistical uncertainty.