We introduce spatially informed variational autoencoders (SI-VAE) as self-supervised deep-learning models that use stochastic point processes to predict spatial organization patterns from images....

We propose a method for variable selection in the intensity function of spatial point processes that combines sparsity-promoting estimation with noise-robust model selection. As high-resolution spatia...

We present a topology grounded, multiscale simulation platform for morphogenesis and biological active matter. Morphogenesis and biological active matter represent keystone problems in biology with ad...

We provide a comprehensive overview of meshfree collocation methods for numerically approximating differential operators on continuously labeled unstructured point clouds. Meshfree collocation methods...

We investigate the computational power of particle methods, a well-established class of algorit hms with applications in scientific computing and computer simulation. The computational power of a comp...

Summary: Image-based 3D modeling of pore-scale extracellular space geometries of the developing zebrafish embryo reveals that the Fgf8a morphogen gradient is regulated by the porous media characterist...

We present a software implementation integrating the time-integration library Odeint from Boost with the OpenFPM framework for scalable scientific computing. This enables compact and scalable codes fo...

Künstliche Intelligenz mit physikalischem Vorwissen soll biologische Vorgänge berechenbar machen

Biologie und Medizin sind eng verknüpft. Künstliche Intelligenz kann dabei in vielen Fragen helfen. Was ist zu beachten?

The IceT library is widely used for parallel compositing but does not support non-convex volume domain decompositions. We provide a backward-compatible extension of IceT to handle non-convex domain de...

We present APR-CNN, a novel class of convolutional neural networks designed for efficient and scalable three-dimensional microscopy image analysis. APR-CNNs operate natively on a sparse...

YouTube video by MEGWARE Computer Chemnitz / Germany

Nonlinearity is required to accurately model many scientific phenomena. This event brings together scientists from a broad array of topics through biology, chemistry, physics and more, to discuss nonl...

We use a combination of unsupervised clustering and sparsity-promoting inference algorithms to learn locally dominant force balances that explain macroscopic pattern formation in self-organized active...

We show that the resolution-dependent loss of bimolecular reactions in spatiotemporal Reaction–Diffusion Master Equations (RDMEs) is in agreement with the mean-

We propose a Gaussian jump process model on a regular Cartesian lattice for the diffusion part of the Reaction–Diffusion Master Equation (RDME). We derive the r