Lowrie, Robert Byron | Search NASA

DOE OSTI2023

Anomalous Behavior of Newtonian Hydrodynamics Coupled with Radiation Transport

This study shows that Newtonian hydrodynamics coupled with radiation transport, using a wide range of methods for treating the material-motion corrections, results in anomalous behavior. In particular, the flow of infinite-medium equilibration will accelerate whenever viewed moving past an observer in the laboratory frame. The acceleration may cause the velocity to increase exponentially in time. An exact fully-relativistic solution is derived to show that there is no acceleration, independent of the reference frame. This solution is analytic and may be used as a validation problem for the simulation of fully-relativistic radiation hydrodynamics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Physics Informed Neural Networks as Computational Physics Emulators

This report is a brief overview and evaluation of Physics Informed Neural Networks (PINNs). Karniadakis and co-workers, e.g., Karniadakis et al. (2021) assert that the PINNs approach integrates seamlessly both data and mathematical physics models, even in partially understood, uncertain and high-dimensional contexts. They further claim that PINNs are effective and efficient for ill-posed and inverse problems, and when combined with domain decomposition, are scalable to large problems and a tool to discover hidden physics. While they demonstrate the capabilities in specific academic instances, their overarching claims about PINNs seem to be an overstatement, at least at the current time. We have briefly considered a few of the limitations of PINNs in this investigation. It is not clear to us if the PINNs approach can ever be competitive with approaches that use specialized algorithms to achieve high-accuracy solutions of governing equations and other techniques that can combine observational data with such solutions. As an example of the latter, consistent with the principles of Bayesian inference, data assimilation, or more generally data-model fusion, is a process that fuses observational data typically with a computational model that respects certain constraints such as conservation laws. For example, improvements in observational network combined with data assimilation have been key in improving weather predictions over the past four decades Kalnay (2003).

97 MATHEMATICS AND COMPUTING↗