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First, we used parameters "orthologs only" and "Fine computation" and second used parameters "orthologs only" and "Fast computation".
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Our brains are no longer the world's finest computation engine.
The results showed that the finer mesh produced typically the same result; however, as the mesh was made finer, the computation time increased significantly.
The recurrent network is an adaptation of RecSOM to the context of the simulation of large scale distributed neural architectures, since it relies on a strictly local fine-grained computation.
This is achieved by conducting a fine-scale computation in subdomains where it is absolutely required (e.g., due to a local breakdown of a continuum model) and coupling it with a coarse-scale computation in the rest of a computational domain.
This way, the coarse-grid problem can be fully overlapped by fine-grid computations (which are embarrassingly parallel) in a wide range of cases.
It is a compute intensive problem: (i) the number of iterations can be quite large to detect small tumors; (ii) many fine-grained computations and discretizations of the object under screening are required for accuracy.
The presented example shows high suitability of the proposed architecture for fine grain numerical computations.
Meanwhile, the hybrid-domain FWI is very suitable for fine-grained parallel computation, and the application of graphic processing unit (GPU) has significantly enhanced the computation efficiency.
Despite advancements in numerical schemes and high-performance computing, the requirements of iterative computations and fine grids hinder further extension of the RE to multi-dimensional and large-scale applications.
It is thus necessary to use numerical methods that are able to capture the fine scale effects with computations on coarse meshes.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com