149,378 research outputs found
Resolving Wave Propagation in Anisotropic Poroelastic Media Using Graphical Processing Units (GPUs)
Biot's equations describe the physics of hydromechanically coupled systems establishing the widely recognized theory of poroelasticity. This theory has a broad range of applications in Earth and biological sciences as well as in engineering. The numerical solution of Biot's equations is challenging because wave propagation and fluid pressure diffusion processes occur simultaneously but feature very different characteristic time scales. Analogous to geophysical data acquisition, high resolution and three dimensional numerical experiments lately redefined state of the art. Tackling high spatial and temporal resolution requires a high-performance computing approach. We developed a multi- graphical processing units (GPU) numerical application to resolve the anisotropic elastodynamic Biot's equations that relies on a conservative numerical scheme to simulate, in a few seconds, wave fields for spatial domains involving more than 1.5 billion grid cells. We present a comprehensive dimensional analysis reducing the number of material parameters needed for the numerical experiments from ten to four. Furthermore, the dimensional analysis emphasizes the key material parameters governing the physics of wave propagation in poroelastic media. We perform a dispersion analysis as function of dimensionless parameters leading to simple and transparent dispersion relations. We then benchmark our numerical solution against an analytical plane wave solution. Finally, we present several numerical modeling experiments, including a three-dimensional simulation of fluid injection into a poroelastic medium. We provide the Matlab, symbolic Maple, and GPU CUDA C routines to reproduce the main presented results. The high efficiency of our numerical implementation makes it readily usable to investigate three-dimensional and high-resolution scenarios of practical applications.ISSN:2169-9313ISSN:0148-0227ISSN:2169-935
Learning to plan with uncertain topological maps
We train an agent to navigate in 3D environments using a hierarchical
strategy including a high-level graph based planner and a local policy. Our
main contribution is a data driven learning based approach for planning under
uncertainty in topological maps, requiring an estimate of shortest paths in
valued graphs with a probabilistic structure. Whereas classical symbolic
algorithms achieve optimal results on noise-less topologies, or optimal results
in a probabilistic sense on graphs with probabilistic structure, we aim to show
that machine learning can overcome missing information in the graph by taking
into account rich high-dimensional node features, for instance visual
information available at each location of the map. Compared to purely learned
neural white box algorithms, we structure our neural model with an inductive
bias for dynamic programming based shortest path algorithms, and we show that a
particular parameterization of our neural model corresponds to the Bellman-Ford
algorithm. By performing an empirical analysis of our method in simulated
photo-realistic 3D environments, we demonstrate that the inclusion of visual
features in the learned neural planner outperforms classical symbolic solutions
for graph based planning.Comment: ECCV 202
Reconstruction of eye movements during blinks
In eye movement research in reading, the amount of data plays a crucial role
for the validation of results. A methodological problem for the analysis of the
eye movement in reading are blinks, when readers close their eyes. Blinking
rate increases with increasing reading time, resulting in high data losses,
especially for older adults or reading impaired subjects. We present a method,
based on the symbolic sequence dynamics of the eye movements, that reconstructs
the horizontal position of the eyes while the reader blinks. The method makes
use of an observed fact that the movements of the eyes before closing or after
opening contain information about the eyes movements during blinks. Test
results indicate that our reconstruction method is superior to methods that use
simpler interpolation approaches. In addition, analyses of the reconstructed
data show no significant deviation from the usual behavior observed in readers
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