\[ \begin{align*} \nabla \cdot \mathbf{E} &= 0 \\ \nabla \cdot \mathbf{B} &= 0 \\ \nabla \times \mathbf{E} &= -\frac{\partial \mathbf{B}}{\partial t} \\ \nabla \times \mathbf{B} &= \frac{\partial \mathbf{E}}{\partial t} \end{align*} \]

Modeling Linear PDE Systems with Probabilistic Machine Learning

Markus Lange-Hegermann
Institut für industrielle Informationstechnik - inIT
Department of Electrical Engineering and Computer Science, TH OWL

Bogdan Raiță
Department of Mathematics and Statistics
Georgetown University

With many, many contributions by Andreas Besginow, Marc Härkönen, Jianlei Huang, Xin Li, and Daniel Robertz

Our goals

  • Use additional knowledge of linear PDE systems with constant coefficients.
  • Construct a computationally nice probabily distribution on such PDE systems.
  • Restricting to this important special case allows more suitable methods.
  • Introduce two such methods: EPGP and S-EPGP.