This dissertation focuses on developing a data-driven framework for analyzing the relative importance of the different physical sub-processes underlying high speed chemically reacting flows. We develop specialized Fourier neural operators (FNOs) that can simultaneously reveal underlying flow physics and achieve sufficient accuracy to serve as potential surrogate models. An embedded linear Fourier layer allows the model to remain interpretable, while the nonlinear encoder-decoder structure provides the flexibility to capture complex physical relationships.