1,336 research outputs found
Neural Vortex Method: from Finite Lagrangian Particles to Infinite Dimensional Eulerian Dynamics
In the field of fluid numerical analysis, there has been a long-standing
problem: lacking of a rigorous mathematical tool to map from a continuous flow
field to discrete vortex particles, hurdling the Lagrangian particles from
inheriting the high resolution of a large-scale Eulerian solver. To tackle this
challenge, we propose a novel learning-based framework, the Neural Vortex
Method (NVM), which builds a neural-network description of the Lagrangian
vortex structures and their interaction dynamics to reconstruct the
high-resolution Eulerian flow field in a physically-precise manner. The key
components of our infrastructure consist of two networks: a vortex
representation network to identify the Lagrangian vortices from a grid-based
velocity field and a vortex interaction network to learn the underlying
governing dynamics of these finite structures. By embedding these two networks
with a vorticity-to-velocity Poisson solver and training its parameters using
the high-fidelity data obtained from high-resolution direct numerical
simulation, we can predict the accurate fluid dynamics on a precision level
that was infeasible for all the previous conventional vortex methods (CVMs). To
the best of our knowledge, our method is the first approach that can utilize
motions of finite particles to learn infinite dimensional dynamic systems. We
demonstrate the efficacy of our method in generating highly accurate prediction
results, with low computational cost, of the leapfrogging vortex rings system,
the turbulence system, and the systems governed by Euler equations with
different external forces
Which user interaction for cross-language information retrieval? Design issues and reflections
A novel and complex form of information access is cross-language information retrieval: searching for texts written in foreign languages based on native language queries. Although the underlying technology for achieving such a search is relatively well understood, the appropriate interface design is not. The authors present three user evaluations undertaken during the iterative design of Clarity, a cross-language retrieval system for low-density languages, and shows how the user-interaction design evolved depending on the results of usability tests. The first test was instrumental to identify weaknesses in both functionalities and interface; the second was run to determine if query translation should be shown or not; the final was a global assessment and focused on user satisfaction criteria. Lessons were learned at every stage of the process leading to a much more informed view of what a cross-language retrieval system should offer to users
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