During my Ph.D. in physics-based simulation, we used the convex optimization technique fast first-order Primal-Dual method [PCBC09] for several complex problems, such as reconstructing both 3D volume and motion of real-world fluid phenomena based on 2D input sequences, which is the inverse problem to forward fluid simulation. We targeted accurate multi-view reconstructions from a sparse number of cameras, which we gather in our data set ScalarFlow, as well as single-view reconstructions. Furthermore, we developed a flexible fluid guiding method and achieved separating boundary conditions for liquids with a common CG solver.
I have been organizing the exercises for the lectures "Basics: Algorithms and Data Structures" and "Deep Learning and Numerical Simulations for Visual Effects" as well as the seminar "Deep Learning in Physics". Additionally, I have been supervising Bachelor's and Master's Theses.
Keywords: fluid simulation, fluid capture, fluid tracking, convex optimization, numeric solvers, neural networks