Learning Intersecting Surfaces via Invariants

A talk by Timon Gutleb, a lecturer in the School of Computer Science, at the University of Leeds, hosted by Leeds Institute for Data Analytics' (LIDA) Scientific Machine Learning (SciML) group. Conical intersections play a crucial role in determining the dynamics of molecules and materials upon light excitation, molecular orbitals and material band structure. In the relevant context of high-dimensional non-smooth hyper-surfaces, common machine learning models perform poorly. Here, we introduce and analyse numerical companion matrix methods for the reconstruction of hypersurfaces with crossings from smooth interpolants given unordered or, without loss of generality, value-sorted data.