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Found 12 papers, 2 papers with code
Learning locally dominant force balances in active particle systems
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 particle systems.
Learning deterministic hydrodynamic equations from stochastic active particle dynamics
We present a principled data-driven strategy for learning deterministic hydrodynamic models directly from stochastic non-equilibrium active particle trajectories.
Parallel Discrete Convolutions on Adaptive Particle Representations of Images
Here, we provide the algorithmic building blocks required to efficiently and natively process APR images using a wide range of algorithms that can be formulated in terms of discrete convolutions.
Inverse-Dirichlet Weighting Enables Reliable Training of Physics Informed Neural Networks
We characterize and remedy a failure mode that may arise from multi-scale dynamics with scale imbalances during training of deep neural networks, such as Physics Informed Neural Networks (PINNs).
STENCIL-NET: Data-driven solution-adaptive discretization of partial differential equations
Often, this requires high-resolution or adaptive discretization grids to capture relevant spatio-temporal features in the PDE solution, e. g., in applications like turbulence, combustion, and shock propagation.
Letter to the Editor and Comments on: Intravital dynamic and correlative imaging reveals diffusion-dominated canalicular and flow-augmented ductular bile flux
Bile, the central metabolic product of the liver, is secreted by hepatocytes into bile canaliculi (BC), tubular subcellular structures of 0. 5-2 $\mu$m diameter which are formed by the apical membranes of juxtaposed hepatocytes.
Learning physically consistent mathematical models from data using group sparsity
We propose a statistical learning framework based on group-sparse regression that can be used to 1) enforce conservation laws, 2) ensure model equivalence, and 3) guarantee symmetries when learning or inferring differential-equation models from measurement data.
A robustness measure for singular point and index estimation in discretized orientation and vector fields
The identification of singular points or topological defects in discretized vector fields occurs in diverse areas ranging from the polarization of the cosmic microwave background to liquid crystals to fingerprint recognition and bio-medical imaging.
Stability selection enables robust learning of partial differential equations from limited noisy data
We show that in particular the combination of stability selection with the iterative hard-thresholding algorithm from compressed sensing provides a fast, parameter-free, and robust computational framework for PDE inference that outperforms previous algorithmic approaches with respect to recovery accuracy, amount of data required, and robustness to noise.
scenery -- Flexible Virtual Reality Visualisation on the Java VM
Life science today involves computational analysis of a large amount and variety of data, such as volumetric data acquired by state-of-the-art microscopes, or mesh data resulting from analysis of such data or simulations.
Graphics
Gradient Distribution Priors for Biomedical Image Processing
We provide motivation for this choice from different points of view, and we fully validate the resulting prior for use on biomedical images by showing its stability and correlation with image quality.
Particle methods enable fast and simple approximation of Sobolev gradients in image segmentation
We show how particle methods as applied to image segmentation allow for a simple and computationally efficient implementation of Sobolev gradients.
