Accelerating urban scale simulations leveraging local spatial 3D structure

[1]  R. Martinez-Cuenca,et al.  Modeling of wastewater treatment processes with hydrosludge , 2021, Water environment research : a research publication of the Water Environment Federation.

[2]  Sangseung Lee,et al.  Analysis of a convolutional neural network for predicting unsteady volume wake flow fields , 2021 .

[3]  Ahsan Kareem,et al.  Emerging frontiers in wind engineering: Computing, stochastics, machine learning and beyond , 2020 .

[4]  Jaime Fern'andez del R'io,et al.  Array programming with NumPy , 2020, Nature.

[5]  Abhinav Vishnu,et al.  CFDNet: a deep learning-based accelerator for fluid simulations , 2020, ICS.

[6]  Jure Leskovec,et al.  Learning to Simulate Complex Physics with Graph Networks , 2020, ICML.

[7]  Shashank Srivastava,et al.  Machine Learning Surrogates for Predicting Response of an Aero-Structural-Sloshing System , 2019, ArXiv.

[8]  Alok Choudhary,et al.  A Real-Time Iterative Machine Learning Approach for Temperature Profile Prediction in Additive Manufacturing Processes , 2019, 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[9]  Petros Koumoutsakos,et al.  Machine Learning for Fluid Mechanics , 2019, Annual Review of Fluid Mechanics.

[10]  Paris Perdikaris,et al.  Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations , 2019, J. Comput. Phys..

[11]  Yike Guo,et al.  A reduced order model for turbulent flows in the urban environment using machine learning , 2019, Building and Environment.

[12]  Nils Thürey,et al.  Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow , 2018, Comput. Graph. Forum.

[13]  J. Nathan Kutz,et al.  Deep learning in fluid dynamics , 2017, Journal of Fluid Mechanics.

[14]  J. Templeton,et al.  Reynolds averaged turbulence modelling using deep neural networks with embedded invariance , 2016, Journal of Fluid Mechanics.

[15]  Wei Li,et al.  Convolutional Neural Networks for Steady Flow Approximation , 2016, KDD.

[16]  Ken Perlin,et al.  Accelerating Eulerian Fluid Simulation With Convolutional Networks , 2016, ICML.

[17]  Andy Davis,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[18]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[19]  N. G. Wright,et al.  Environmental Applications of Computational Fluid Dynamics , 2013 .

[20]  B. Koren,et al.  Review of computational fluid dynamics for wind turbine wake aerodynamics , 2011 .

[21]  Xinyang Jin,et al.  New inflow boundary conditions for modelling the neutral equilibrium atmospheric boundary layer in computational wind engineering , 2009 .

[22]  Yoshihide Tominaga,et al.  AIJ guidelines for practical applications of CFD to pedestrian wind environment around buildings , 2008 .

[23]  Arnaud Cockx,et al.  Oxygen transfer prediction in aeration tanks using CFD , 2007 .

[24]  Yoshihide Tominaga,et al.  Cooperative project for CFD prediction of pedestrian wind environment in the Architectural Institute of Japan , 2007 .

[25]  Antoine Guelfi,et al.  NEPTUNE: A New Software Platform for Advanced Nuclear Thermal Hydraulics , 2007 .

[26]  Da-Wen Sun,et al.  Computational fluid dynamics (CFD) ¿ an effective and efficient design and analysis tool for the food industry: A review , 2006 .

[27]  L. Morawska,et al.  A review of dispersion modelling and its application to the dispersion of particles : An overview of different dispersion models available , 2006 .

[28]  Jyeshtharaj B. Joshi,et al.  Effect of impeller design on the flow pattern and mixing in stirred tanks , 2006 .

[29]  Hrvoje Jasak,et al.  A tensorial approach to computational continuum mechanics using object-oriented techniques , 1998 .

[30]  P. Richards,et al.  Appropriate boundary conditions for computational wind engineering models using the k-ε turbulence model , 1993 .

[31]  Yuwei Li,et al.  Dynamic overset CFD simulations of wind turbine aerodynamics , 2012 .