Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics | Paper Citation Record

Observation a34aa595-2055-43d1-bffe-1a08dc7d697d · outbound

This paper cites Stankovic, B.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Stankovic, B

Reference 1

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Observation 46b42d65-e2e1-4e2e-8853-3790a8a079bd · outbound

This paper cites Miyazaki, K.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Miyazaki, K

Reference 2

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This paper cites Sughimoto, Y.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Sughimoto, Y

Reference 3

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This paper cites Markl, A.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Markl, A

Reference 4

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This paper cites Casas, J.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Casas, J

Reference 5

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Unresolved cited work

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Unresolved cited work

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Observation 6033212c-9709-42ce-8b3b-12eb8d6edc57 · outbound

This paper cites Zingaro, L.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Zingaro, L

Reference 8

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Unresolved cited work

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Unresolved cited work

Reference 10

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Unresolved cited work

Reference 11

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Observation 8ddf2e5a-cded-41a1-921f-52fd18abf779 · outbound

This paper cites Zingaro, I.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Zingaro, I

Reference 12

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Observation fbbadd9e-71bb-4b2d-b99e-eefa4af37160 · outbound

This paper cites Futami, T.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Futami, T

Reference 13

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Unresolved cited work

Reference 14

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This paper cites Quarteroni, Numerical Models for Differential Problems, volume 16 ofModeling, Simulation and Applications, 3rd ed., Springer International Publishing, Cham, Switzerland, 2017.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Quarteroni, Numerical Models for Differential Problems, volume 16 ofModeling, Simulation and Applications, 3rd ed., Springer International Publishing, Cham, Switzerland, 2017

Reference 15

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This paper cites Totorean, I.-C.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Totorean, I.-C

Reference 16

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This paper cites Goodfellow, Y.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Goodfellow, Y

Reference 17

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This paper cites Tassi, A.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Tassi, A

Reference 18

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This paper cites Quarteroni, P.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Quarteroni, P

Reference 19

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Unresolved cited work

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This paper cites Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations

Reference 21

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Unresolved cited work

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Observation 8084acf4-ab90-4a5c-a146-c86f11eea074 · outbound

This paper cites Zhang, M.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Zhang, M

Reference 23

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Ferdian, D

Reference 24

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This paper cites Zhang, S.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Zhang, S

Reference 25

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Unresolved cited work

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This paper cites Kissas, Y.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Kissas, Y

Reference 27

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Sarabian, H

Reference 28

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This paper cites URL:https://www.cambridge.org/core/ product/identifier/S002211202100135X/type/journal_article.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics URL:https://www.cambridge.org/core/ product/identifier/S002211202100135X/type/journal_article

Reference 29

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This paper cites Hub, Benchmark dataset for validating computational fluid dynamic (CFD) simulation of blood flow through FDA nozzle and FDA blood pump — NCI Hub, 2022.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Hub, Benchmark dataset for validating computational fluid dynamic (CFD) simulation of blood flow through FDA nozzle and FDA blood pump — NCI Hub, 2022

Reference 30

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Unresolved cited work

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Hariharan, M

Reference 32

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This paper cites Hariharan, R.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Hariharan, R

Reference 33

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This paper cites Quarteroni, L.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Quarteroni, L

Reference 34

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Bischof, M

Reference 35

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Kissas, E

Reference 36

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This paper cites Chollet, Deep Learning with Python, Simon and Schuster, 2021.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Chollet, Deep Learning with Python, Simon and Schuster, 2021

Reference 37

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This paper cites Universal Solution Manifold Networks (USM-Nets): non-intrusive mesh-free surrogate models for problems in variable domains.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Universal Solution Manifold Networks (USM-Nets): non-intrusive mesh-free surrogate models for problems in variable domains

Reference 38

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This paper cites Adam: A Method for Stochastic Optimization.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Adam: A Method for Stochastic Optimization

Reference 39

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Unresolved cited work

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Unresolved cited work

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This paper cites Arndt, W.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Arndt, W

Reference 43

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Unresolved cited work

Reference 44

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This paper cites Forti, L.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Forti, L

Reference 45

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This paper cites URL:https://mox.polimi.it/research-areas/hpcmox/ hardware/, [Accessed 13-Aug-2022].

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics URL:https://mox.polimi.it/research-areas/hpcmox/ hardware/, [Accessed 13-Aug-2022]

Reference 46

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Reference 47

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This paper cites Géron, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems, O’Reilly Media, Inc., 2019.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Géron, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems, O’Reilly Media, Inc., 2019

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Ioffe, C

Reference 49

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This paper cites Glorot, Y.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Glorot, Y

Reference 50

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This paper cites Raschka, Python Machine Learning, Packt publishing ltd, 2015.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Raschka, Python Machine Learning, Packt publishing ltd, 2015

Reference 51

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Abadi, P

Reference 52

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Chollet, et al., Keras,https://keras.io, 2015

Reference 53

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Ahrens, B

Reference 54

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Unresolved cited work

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Weiss, V

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Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Unresolved cited work

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This paper cites Henry, J.

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics Henry, J

Reference 58

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