Variational inference with Gaussian mixture models (GMMs) enables learning of highly
tractable yet multi-modal approximations of intractable target distributions with up to a few …

The Finite Element Method, an important technique in engineering, is aided by Adaptive Mesh
Refinement (AMR), which dynamically refines mesh regions to allow for a favorable trade-…

The notion of symbiosis has been increasingly mentioned in research on physically coupled
human-machine systems. Yet, a uniform specification on which aspects constitute human-…

Stochastic gradient-based optimization is crucial to optimize neural networks. While popular
approaches heuristically adapt the step size and direction by rescaling gradients, a more …

Simulating physical systems is essential in engineering, but analytical solutions are limited
to straightforward problems. Consequently, numerical methods like the Finite Element …

Bayesian meta-learning (BML) enables fitting expressive generative models to small datasets
by incorporating inductive priors learned from a set of related tasks. The Neural Process (…

Many engineering systems require accurate simulations of complex physical systems. Yet,
analytical solutions are only available for simple problems, necessitating numerical …

Variational inference with full-covariance Gaussian approximations is an important line of
research, as such Gaussian variational approximations (GVAs) allow for tractable approximate …

Simulating dynamic physical interactions is a critical challenge across multiple scientific
domains, with applications ranging from robotics to material science. For mesh-based …

Despite continuous investment in road and vehicle safety, as well as improvements in
technology standards, the total amount of road traffic accidents has been increasing over the last …