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Finding the distribution of the velocities and pressures of a fluid by solving the Navier-Stokes equations is a principal task in the chemical, energy, and pharmaceutical industries, as well as in mechanical engineering and the design of pipeline systems.
Differential equations with applications and historical notes
George Finlay Simmons · 1972
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Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1986
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Large sample properties of simulations using latin hypercube sampling
Michael Stein · 1987
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Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell B. Stinchcombe, and Halbert L. White · 1989
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Computational fluid dynamics
John David Anderson and John Wendt · 1995
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Quantum computation and quantum information, 2002
Michael A Nielsen and Isaac Chuang · 2002
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Meshless methods for computational fluid dynamics
Aaron Jon Katz · 2009
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Construction and comparison of high-dimensional Sobol’generators
Ilya M Sobol’, Danil Asotsky, Alexander Kreinin, and Sergei Kucherenko · 2011
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QBoost: Large scale classifier training with adiabatic quantum optimization
Hartmut Neven, Vasil S. Denchev, Geordie Rose, and William G. Macready · 2012
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vofoam - a geometrical volume of fluid algorithm on arbitrary unstructured meshes with local dynamic adaptive mesh refinement using OpenFOAM
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Sebastian Ruder · 2016
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Mohd Hafiz Zawawi, A Saleha, A Salwa, NH Hassan, Nazirul Mubin Zahari, Mohd Zakwan Ramli, and Zakaria Che Muda · 2018
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Philipp Grohs, Fabian Hornung, Arnulf Jentzen, and Philippe Von Wurstemberger · 2018
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Machine learning & artificial intelligence in the quantum domain: a review of recent progress
Vedran Dunjko and Hans J Briegel · 2018
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Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind · 2018
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Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
Stefan Elfwing, Eiji Uchibe, and Kenji Doya · 2018
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Quantum computing in the nisq era and beyond
John Preskill · 2018
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Maziar Raissi, Paris Perdikaris, and George E Karniadakis · 2019
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Chen Zhao and Xiao-Shan Gao · 2019
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Unstructured un-split geometrical volume-of-fluid methods - A review
Tomislav Marić, Douglas B. Kotheb, and Dieter Bothe · 2020
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Behnam Neyshabur, Hanie Sedghi, and Chiyuan Zhang · 2020
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Maria Schuld, Alex Bocharov, Krysta M Svore, and Nathan Wiebe · 2020
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Michael Perelshtein, Asel Sagingalieva, Karan Pinto, Vishal Shete, Alexey Pakhomchik, Artem Melnikov, Florian Neukart, Georg Gesek, Alexey Melnikov, and Valerii Vinokur · 2022
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Notes on computational fluid dynamics: General principles
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Experimental quantum speed-up in reinforcement learning agents
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