In the first half of the talk, I will briefly survey the theory of matroids with coefficients, which was introduced by Andreas Dress and Walter Wenzel in the 1980s and refined by the speaker and Nathan Bowler in 2016. This theory provides a unification of vector subspaces, matroids, valuated matroids, and oriented matroids. Then, in the second half, I will outline an intriguing connection between Lorentzian polynomials, as defined by Petter Brändén and June Huh, and matroids with coefficients.  The second part of the talk represents joint work with June Huh, Mario Kummer, and Oliver Lorscheid.

We present an exponential-integrator-based multi-index Monte Carlo (MIMC) method for the weak approximation of mild solutions to semilinear stochastic partial differential equations (SPDEs). Theoretical results on multi-index coupled solutions of the SPDE are provided, demonstrating their stability and the satisfaction of multiplicative error estimates. Leveraging this theory, we develop a tractable MIMC algorithm. Numerical experiments illustrate that MIMC outperforms alternative approaches, such as multilevel Monte Carlo, particularly in low-regularity settings.