Results 31 to 40 of about 666,598 (288)
High Voltage, 2023 In order to achieve high performance for solving the ion flow field problem, an approach is proposed with the tensor‐structured finite element method (FEM) to accelerate the Newton iteration.
Qiwen Cheng, Jun Zou
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On the Optimal Linear Contraction Order of Tree Tensor Networks, and Beyond [PDF]
SIAM Journal on Scientific Computing, 2022 The contraction cost of a tensor network depends on the contraction order. However, the optimal contraction ordering problem is known to be NP-hard. We show that the linear contraction ordering problem for tree tensor networks admits a polynomial-time ...
Mihail Stoian +2 more
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Fast counting with tensor networks
SciPost Physics, 2019 We introduce tensor network contraction algorithms for counting satisfying assignments of constraint satisfaction problems (#CSPs). We represent each arbitrary #CSP formula as a tensor network, whose full contraction yields the number of satisfying ...
Stefanos Kourtis, Claudio Chamon, Eduardo R. Mucciolo, Andrei E. Ruckenstein
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Effect of myofibre architecture on ventricular pump function by using a neonatal porcine heart model: from DT-MRI to rule-based methods [PDF]
Royal Society Open Science, 2020 Myofibre architecture is one of the essential components when constructing personalized cardiac models. In this study, we develop a neonatal porcine bi-ventricle model with three different myofibre architectures for the left ventricle (LV).
Debao Guan +3 more
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Benchmarking treewidth as a practical component of tensor network simulations.
PLoS ONE, 2018 Tensor networks are powerful factorization techniques which reduce resource requirements for numerically simulating principal quantum many-body systems and algorithms.
Eugene F Dumitrescu +5 more
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Distributed-memory multi-GPU block-sparse tensor contraction for electronic structure
IEEE International Parallel and Distributed Processing Symposium, 2020 Many domains of scientific simulation (chemistry, condensed matter physics, data science) increasingly eschew dense tensors for block-sparse tensors, sometimes with additional structure (recursive hierarchy, rank sparsity, etc.).
T. Hérault +6 more
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High-Performance Tensor Contraction without Transposition [PDF]
SIAM Journal on Scientific Computing, 2016 Tensor computations---in particular tensor contraction (TC)---are important kernels in many scientific computing applications. Due to the fundamental similarity of TC to matrix multiplication and to the availability of optimized implementations such as ...
D. Matthews
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Grassmann higher-order tensor renormalization group approach for two-dimensional strong-coupling QCD
Nuclear Physics B, 2023 We present a tensor-network approach for two-dimensional strong-coupling QCD with staggered quarks at nonzero chemical potential. After integrating out the gauge fields at infinite coupling, the partition function can be written as a full contraction of ...
Jacques Bloch, Robert Lohmayer
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Tensor Contraction Layers for Parsimonious Deep Nets [PDF]
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 Tensors offer a natural representation for many kinds of data frequently encountered in machine learning. Images, for example, are naturally represented as third order tensors, where the modes correspond to height, width, and channels.
Jean Kossaifi +4 more
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Efficient computation of the second-Born self-energy using tensor-contraction operations. [PDF]
Journal of Chemical Physics, 2019 In the nonequilibrium Green's function approach, the approximation of the correlation self-energy at the second-Born level is of particular interest, since it allows for a maximal speed-up in computational scaling when used together with the generalized ...
R. Tuovinen, F. Covito, Michael A Sentef
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