4 papers · 1 filter
Hierarchical Bayesian Quadrature
Tim Weiland, Toni Karvonen, Philipp Hennig
Numerical integration is a cornerstone of various scientific computing applications, such as engineering simulations and model evidence computations in probabilistic machine learni…
Scalable Bayesian Inference for Nonlinear Conservation Laws
Tim Weiland, Philipp Hennig
Nonlinear conservation laws are at the heart of many of the most important dynamical systems in science and engineering. In practical applications, such systems are often subject t…
Flexible and Efficient Probabilistic PDE Solvers through Gaussian Markov Random Fields
Tim Weiland, Marvin Pförtner, Philipp Hennig
Mechanistic knowledge about the physical world is virtually always expressed via partial differential equations (PDEs). Recently, there has been a surge of interest in probabilisti…
Scaling up Probabilistic PDE Simulators with Structured Volumetric Information
Tim Weiland, Marvin Pförtner, Philipp Hennig
Modeling real-world problems with partial differential equations (PDEs) is a prominent topic in scientific machine learning. Classic solvers for this task continue to play a centra…