67,239 research outputs found
Tomographic Image Reconstruction of Fan-Beam Projections with Equidistant Detectors using Partially Connected Neural Networks
We present a neural network approach for tomographic imaging problem using interpolation methods and fan-beam projections. This approach uses a partially connected neural network especially assembled for solving tomographic\ud
reconstruction with no need of training. We extended the calculations to perform reconstruction with interpolation and to allow tomography of fan-beam geometry. The main goal is to aggregate speed while maintaining or improving the quality of the tomographic reconstruction process
Modelling Non-Markovian Quantum Processes with Recurrent Neural Networks
Quantum systems interacting with an unknown environment are notoriously
difficult to model, especially in presence of non-Markovian and
non-perturbative effects. Here we introduce a neural network based approach,
which has the mathematical simplicity of the
Gorini-Kossakowski-Sudarshan-Lindblad master equation, but is able to model
non-Markovian effects in different regimes. This is achieved by using recurrent
neural networks for defining Lindblad operators that can keep track of memory
effects. Building upon this framework, we also introduce a neural network
architecture that is able to reproduce the entire quantum evolution, given an
initial state. As an application we study how to train these models for quantum
process tomography, showing that recurrent neural networks are accurate over
different times and regimes.Comment: 10 pages, 8 figure
Neural Network based Ionsopheric Tomography
第4回極域科学シンポジウム個別セッション:[OS] 宙空圏11月14日(木)〜15日(金)国立極地研究所 2階大会議室前ラウン
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