Search Results for author: Ivo F. Sbalzarini

Found 9 papers, 2 papers with code

Inverse-Dirichlet Weighting Enables Reliable Training of Physics Informed Neural Networks

no code implementations2 Jul 2021 Suryanarayana Maddu, Dominik Sturm, Christian L. Müller, Ivo F. Sbalzarini

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

no code implementations15 Jan 2021 Suryanarayana Maddu, Dominik Sturm, Bevan L. Cheeseman, Christian L. Müller, Ivo F. Sbalzarini

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

no code implementations22 Dec 2020 Lutz Brusch, Yannis Kalaidzidis, Kirstin Meyer, Ivo F. Sbalzarini, Marino Zerial

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

no code implementations11 Dec 2020 Suryanarayana Maddu, Bevan L. Cheeseman, Christian L. Müller, Ivo F. Sbalzarini

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

no code implementations30 Sep 2020 Karl B. Hoffmann, Ivo F. Sbalzarini

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

1 code implementation17 Jul 2019 Suryanarayana Maddu, Bevan L. Cheeseman, Ivo F. Sbalzarini, Christian L. Müller

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.

Model Selection

scenery -- Flexible Virtual Reality Visualisation on the Java VM

2 code implementations16 Jun 2019 Ulrik Günther, Tobias Pietzsch, Aryaman Gupta, Kyle I. S. Harrington, Pavel Tomancak, Stefan Gumhold, Ivo F. Sbalzarini

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

no code implementations13 Aug 2014 Yuanhao Gong, Ivo F. Sbalzarini

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.

Denoising

Particle methods enable fast and simple approximation of Sobolev gradients in image segmentation

no code implementations2 Mar 2014 Ivo F. Sbalzarini, Sophie Schneider, Janick Cardinale

We show how particle methods as applied to image segmentation allow for a simple and computationally efficient implementation of Sobolev gradients.

Bayesian Inference Semantic Segmentation

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