Learning thermodynamically constrained equations of state with uncertainty

arXiv:2306.17004v2 Announce Type: replace-cross
Abstract: Numerical simulations of high energy-density experiments require equation of state (EOS) models that relate a material's thermodynamic state variables -- specifically pressure, volume/density, energy, and temperature. EOS models are typically constructed using a semi-empirical parametric methodology, which assumes a physics-informed functional form with many tunable parameters calibrated using experimental/simulation data. Since there are inherent uncertainties in the calibration data (parametric uncertainty) and the assumed functional EOS form (model uncertainty), it is essential to perform uncertainty …

abstract arxiv energy eos equation form functional material methodology numerical parameters parametric physics physics.data-an physics-informed simulations state stat.ml type uncertainty variables