We introduce dynamically warping grids for adaptive liquid simulation. Our primary contributions are a strategy for dynamically deforming regular grids over the course of a simulation and a method for efficiently utilizing these deforming grids for liquid simulation. Prior work has shown that unstructured grids are very effective for adaptive fluid simulations. However, unstructured grids often lead to complicated implementations and a poor cache hit rate due to inconsistent memory access. Regular grids, on the other hand, provide a fast, fixed memory access pattern and straightforward implementation. Our method combines the advantages of both: we leverage the simplicity of regular grids while still achieving practical and controllable spatial adaptivity. We demonstrate that our method enables adaptive simulations that are fast, flexible, and robust to null-space issues. At the same time, our method is simple to implement and takes advantage of existing highly-tuned algorithms.
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http://dx.doi.org/10.1109/TVCG.2018.2883628 | DOI Listing |
J Chem Phys
December 2024
Faculty of Computer Science, Dresden University of Technology, Dresden, Germany.
We show that the resolution-dependent loss of bimolecular reactions in spatiotemporal Reaction-Diffusion Master Equations (RDMEs) is in agreement with the mean-field Collins-Kimball (C-K) theory of diffusion-limited reaction kinetics. The RDME is a spatial generalization of the chemical master equation, which enables studying stochastic reaction dynamics in spatially heterogeneous systems. It uses a regular Cartesian grid to partition space into locally well-mixed reaction compartments and treats diffusion as a jump reaction between neighboring grid cells.
View Article and Find Full Text PDFJ Appl Crystallogr
December 2024
Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OxfordOX1 3QG, United Kingdom.
Analytical absorption corrections are employed in scaling diffraction data for highly absorbing samples, such as those used in long-wavelength crystallography, where empirical corrections pose a challenge. is an accelerated software package developed to calculate analytical absorption corrections. It accomplishes this by ray-tracing the paths of diffracted X-rays through a voxelized 3D model of the sample.
View Article and Find Full Text PDFJ Acoust Soc Am
December 2024
Institute of Geophysics, Department of Earth and Planetary Sciences, ETH Zürich, 8092 Zürich, Switzerland.
This work explores techniques for accurately modeling the propagation of ultrasound waves in lossy fluid-solid media, such as within transcranial ultrasound, using the spectral-element method. The objectives of this work are twofold, namely, (1) to present a formulation of the coupled viscoacoustic-viscoelastic wave equation for the spectral-element method in order to incorporate attenuation in both fluid and solid regions and (2) to provide an end-to-end workflow for performing spectral-element simulations in transcranial ultrasound. The matrix-free implementation of this high-order finite-element method is very well-suited for performing waveform-based ultrasound simulations for both transcranial imaging and focused ultrasound treatment thanks to its excellent accuracy, flexibility for dealing with complex geometries, and computational efficiency.
View Article and Find Full Text PDFBiol Direct
November 2024
Institute of Biotechnology, National Tsing Hua University, Hsinchu, Taiwan.
Background: Mitochondria are highly dynamic organelles that constantly undergo processes of fission and fusion. The changes in mitochondrial dynamics shape the organellar morphology and influence cellular activity regulation. Soft X-ray tomography (SXT) allows for three-dimensional imaging of cellular structures while they remain in their natural, hydrated state, which omits the need for cell fixation and sectioning.
View Article and Find Full Text PDFNeural Netw
February 2025
School of Computer Science and Technology, Heilongjiang University, Harbin, 150000, China. Electronic address:
Medical image segmentation is essential for accurately representing tissues and organs in scans, improving diagnosis, guiding treatment, enabling quantitative analysis, and advancing AI-assisted healthcare. Organs and lesion areas in medical images have complex geometries and spatial relationships. Due to variations in the size and location of lesion areas, automatic segmentation faces significant challenges.
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