Solving Irregular and Data-enriched Differential Equations using Deep Neural Networks
This work addresses challenges in numerical PDEs for researchers in computational physics and engineering, but it is incremental as it reviews and extends existing methods.
The paper tackles solving irregular solutions in partial differential equations (PDEs) using deep neural networks, extending a method to analyze shocks in compressible Euler and magnetohydrodynamics equations, and demonstrates performance improvements and parameter space exploration with synthetic experimental data integration.
Recent work has introduced a simple numerical method for solving partial differential equations (PDEs) with deep neural networks (DNNs). This paper reviews and extends the method while applying it to analyze one of the most fundamental features in numerical PDEs and nonlinear analysis: irregular solutions. First, the Sod shock tube solution to compressible Euler equations is discussed, analyzed, and then compared to conventional finite element and finite volume methods. These methods are extended to consider performance improvements and simultaneous parameter space exploration. Next, a shock solution to compressible magnetohydrodynamics (MHD) is solved for, and used in a scenario where experimental data is utilized to enhance a PDE system that is \emph{a priori} insufficient to validate against the observed/experimental data. This is accomplished by enriching the model PDE system with source terms and using supervised training on synthetic experimental data. The resulting DNN framework for PDEs seems to demonstrate almost fantastical ease of system prototyping, natural integration of large data sets (be they synthetic or experimental), all while simultaneously enabling single-pass exploration of the entire parameter space.