Towards Sustainable Architecture: 3D ConvolutionalNeural Networks for Computational Fluid DynamicsSimulation and Reverse Design WorkflowJosef Musil*, Jakub Knir, Athanasios Vitsas, Irene GallouSpecialist Modelling GroupFoster PartnersLondon, UKemail@example.comAbstractWe present a general and flexible approximation model for near real-time predictionof steady turbulent flow in a 3D domain based on residual Convolutional NeuralNetworks (CNNs). This approach can provide immediate feedback for real-timeiterations at the early stage of architectural design. This work-flow is then reversedand offers a designer a tool that generates building volumes based on target windflow.1IntroductionArchitectural design depends on environmental constraints from its initial stage, when buildingsand cities take shape and informed decisions on sustainable development are especially important.However, proposals can change rapidly and it is challenging to provide relevant simulations at thesame pace. Especially, Computational Fluid Dynamics (CFD) requires complex geometry preparationand computationally demanding solutions. This is time consuming and in contradiction to the speedof design progress. To facilitate the impact of CFD on design, this work focuses on data-driven flowfield prediction and leverages the approximation using CNNs.Previous work shows good results in fast simulation of fluid dynamics [1, 5, 6] and approximation ofNavier-Stokes equations . We focus on the application in architecture in 3D domains using CNNswith residual blocks . Our further contribution is to experiment with reverse training to predictarchitectural volumes. Fast forward prediction has a significant potential to improve sustainabledesign, yet still to inform the design by the results of a CFD analysis depends on the creativity of thedesigner and there is no direct way to inform design decisions other than choosing the best design outof a high number of proposals. We facilitate this using the same CNN model trained in the reversedirection.2Data Creation and SimulationVarious geometries representing samples of urban structures were generated using a visual programming language and common Computer Aided Design (CAD) software. These samples considerstandard distribution of building heights in a city, and width and depths were constrained to commonminimum and maximum sizes. Each sample is represented as a 3D mesh and has to fit inside avolume with dimensions of 256m x 128m x 64m. Meshes are voxelised with 1m resolution. Our totalset had 3500 samples: 3325 (95%) for training and 175 (5%) for testing.In aerodynamics related design, analysis and optimisation problems, flow fields are simulated usingCFD solvers. However, CFD simulation is usually a computationally expensive, memory demanding33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada.
Ground Truth (CFD Simulation / 3D Mesh)CNN PredictionSectionReverse DirectionPlanForward DirectionSectionPlan3D Input (3D Mesh / Thresholded 3D CFD)Figure 1: Forward (top) and reverse (bottom) prediction.and time consuming iterative process. These drawbacks of CFD limit opportunities for design spaceexploration and interactive design. Our data set was simulated using OpenFOAM software. Due tothe number of cases needed for CNN training the workflow was fully automated.3Neural Network ArchitectureOur network architecture has a U-net shape with eight encoder and seven decoder layers. Eachlayer consists of one residual block with one 3D convolution with stride 2 filters 4x4x4 and one 3Dconvolution with stride 1 and filter 3x3x3. Our tests showed these gated blocks improved our resultscompared to plain encoder decoder architecture . Activations are concatenated exponential linearunits. Additionally, this fully connected CNN provides good generalisation properties for geometriesdifferent from the training set and for input data larger than the training sample dimensions. Thisnetwork can estimate the wind velocity field three orders of magnitude faster than a CFD solver bothin 3D domain. The test mean squared error loss kept improving throughout 1000 epochs for bothdirections and proved generalisation ability. In reverse direction we change the number of outputchannels to 1 (1 represents building, 0 outside space), compared to 3 output channels in forwarddirection (x, y, z components of wind direction vectors).4ResultsWe implemented a Flask server to interactively run prediction from a common CAD software Rhino,and its visual programming interface Grasshopper. This CAD provides visualization tools that wereused to produce sample images. We show one sample of forward CFD prediction of the wind velocitymagnitude (calculated as Froberius norm of x,y,z components) and one of reverse prediction ofvolumes in Figure 1. Yellow is undesirable high wind speed, blue is preferred low wind speed.5DiscussionIn concept design stages, fast analysis responses are a critical objective across the industries. As nearreal time prediction proves valuable, the proposed methodology has potential applications outside thearchitecture. In reverse order, it shifts designer’s focus directly onto the desired effect and humanwell-being to use their time efficiently towards sustainable design. In our further research, we arelooking at improving the cost function by adding continuity equation error and implementing agenerative adversarial network. We also explore possibilities to generate multiple building predictionsfor a single input wind flow.2
Ethical ImplicationsThis work does not involve major ethical issues. Only in the case of building a future general model,enough samples from variety of cultural and architectural styles and typologies should be considered.AcknowledgmentsWe would like to thank to Samuel Wilkinson for his scientific reviews.References Xiaoxiao Guo, Wei Li, and Francesco Iorio. Convolutional Neural Networks for Steady FlowApproximation. pages 481–490. Association for Computing Machinery (ACM), aug 2016. doi:10.1145/2939672.2939738. Phillip Isola, Jun Yan Zhu, Tinghui Zhou, and Alexei A. Efros. Image-to-image translation withconditional adversarial networks. Proceedings - 30th IEEE Conference on Computer Vision andPattern Recognition, CVPR 2017, 2017-January:5967–5976, 2017. doi: 10.1109/CVPR.2017.632. Tharindu P. Miyanawala and Rajeev K. Jaiman. An Efficient Deep Learning Technique forthe Navier-Stokes Equations: Application to Unsteady Wake Flow Dynamics. 2017. URLhttp://arxiv.org/abs/1710.09099. Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, and Ken Perlin. Acceleratingeulerian fluid simulation with convolutional networks. 34th International Conference on MachineLearning, ICML 2017, 7:5258–5267, 2017. Cristina White, Daniela Ushizima, and Charbel Farhat. Neural Networks Predict Fluid DynamicsSolutions from Tiny Datasets. 2019. URL http://arxiv.org/abs/1902.00091. Samuel Wilkinson and Sean Hanna. Approximating computational fluid dynamics for generativetall building design. International Journal of Architectural Computing, 12(2):155–177, 2014.AppendixDemoWe present a short animated video that demonstrates this paper’s work. The demo covers the wholedesign and simulation process as seen in the links below:https://youtu.be/GfJS8AI0omY and https://youtu.be/rKO4wei3Dos3
Supplementary MaterialsA Case StudyWe present an example of a designer’s workflow using our forward and reverse network to design andoptimise urban volumes that would produce desired wind flow. A fictional site layout is visualised.This demo’s bounding box’s width and depth is 256 metres and maximum height is 64 meters. Thisis twice as large as our training set and shows the advantage of using a CNN.Our neural network code is implemented using Tensorflow 2.0 and its Keras module. Communicationbetween our CAD software and Tensorflow is by http requests responded by a Flask server. In ourcurrent setup, the bottleneck is pre-processing geometry, that needs to be voxelised. This can be spedup in the future using external mesh libraries.A.1 Initial Sketch of VolumesInitial sketches of urban volumes can be drawn in a CAD software to provide a desired idea of thedesign and produce initial CFD analysis. Optionally this step can be omitted and a designer candirectly draw a point-cloud representing areas with slow wind (step A.3).A.2 Initial Interactive CFD AnalysisFigure 2: Initial CFD.Our forward trained network provides a spatial CFD analysis prediction within a few seconds and isvisualised in our CAD software.A.3 Thresholded and Modified CFD AnalysisFigure 3: Threshold.The CFD is thresholded to localise only areas with low wind speeds. These are areas better-suited foroutdoor activity like sitting and running. A point-cloud represents these areas, which can be freelymodified using geometry transformations to design desired wind effect.4
A.4 Geometry PredictionFigure 4: Geometry prediction.Our reverse trained network predicts urban volumes that will produce our target wind flow and canbe exported as a mesh object.A.5 Final CFD AnalysisFigure 5: Final CFD.Predicted volumes are used to do a CFD prediction of the wind flow.A.6 DiscussionIn future research we will look at interior spaces, in which case passive cooling plays a significantrole in minimising energy consumption. Input for reverse direction would improve if pedestriancomfort was used for example.Our current limitation is that only one direction of wind is considered. This is applicable in areaswhere there is a dominant wind direction. In other areas, multiple directions (usually 36) must beconsidered. Our forward trained network can predict all those directions and will be combined then.5
A.2 Initial Interactive CFD Analysis Figure 2: Initial CFD. Our forward trained network provides a spatial CFD analysis prediction within a few seconds and is visualised in our CAD software. A.3 Thresholded and Modiﬁed CFD Analysis Figure 3: Threshold. The CFD is thresholded to localise on
Learning a Deep Convolutional Network for Image Super-Resolution . a deep convolutional neural network (CNN)  that takes the low- . Convolutional Neural Networks. Convolutional neural networks (CNN) date back decades  and have recently shown an explosive popularity par-
ImageNet Classification with Deep Convolutional Neural Networks, NIPS 2012 M. Zeiler and R. Fergus, Visualizing and Understanding Convolutional Networks, ECCV 2014 K. Simonyan and A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, ICLR 2015
2 Convolutional neural networks CNNs are hierarchical neural networks whose convolutional layers alternate with subsampling layers, reminiscent of sim-ple and complex cells in the primary visual cortex [Wiesel and Hubel, 1959]. CNNs vary in how convolutional and sub-sampling layers are realized and how the nets are trained. 2.1 Image processing .
Oct 04, 2010 · Table 8-1: Examples of generator polynomials for rate 1/2 convolutional codes with different constraint lengths. Figure 8-2: Block diagram view of convolutional coding with shift registers. 8.3 Two Views of the Convolutional Encoder We now describe two views
What is Computer Architecture? “Computer Architecture is the science and art of selecting and interconnecting hardware components to create computers that meet functional, performance and cost goals.” - WWW Computer Architecture Page An analogy to architecture of File Size: 1MBPage Count: 12Explore further(PDF) Lecture Notes on Computer Architecturewww.researchgate.netComputer Architecture - an overview ScienceDirect Topicswww.sciencedirect.comWhat is Computer Architecture? - Definition from Techopediawww.techopedia.com1. An Introduction to Computer Architecture - Designing .www.oreilly.comWhat is Computer Architecture? - University of Washingtoncourses.cs.washington.eduRecommended to you b
of networks are updated according to learning rate, cost function via stochastic gradient descent during the back propagation. In the following, we brieﬂy introduce the structures of di erent DNNs applied in NLP tasks. 2.1.1 Convolutional Neural Network Convolutional neural networks (CNNs) learn local features and assume that these features
Convolutional Neural Network is very powerful for analyzing visual image. The convolutional layers can capture the local features. The pooling layers can concentrate the local changes, as well as reduce the noise and data size. The full connected layers can combine all the local features to generate global features .
The convolutional layer Ingredient II: Max-Pooling Simply join e.g. 2x2 adjacent pixels in one. Hinton: „The pooling operation used in convolutional neural networks is a big mistake and the fact that it works so well is a disaster“ Also sliding window versions.
C. Classiﬁcation layer Kernel sizes of convolutional ﬁlters and max-pooling rect-angles are chosen such that either the output maps of the last convolutional layer are down-sampled to 1 pixel per map, or a fully connected layer combines the outputs of the last convolutional layer into a 1D feature vector. The last layer is
ImageNet Classiﬁcation with Deep Convolutional Neural Networks Alex Krizhevsky University of Toronto firstname.lastname@example.org Ilya Sutskever University of Toronto email@example.com Geoffrey E. Hinton University of Toronto firstname.lastname@example.org Abstract We trained a large, deep convolutional neural network to classify the 1.2 million
Image Colorization with Deep Convolutional Neural Networks Jeff Hwang email@example.com You Zhou firstname.lastname@example.org Abstract We present a convolutional-neural-network-based sys-tem that faithfully colorizes black and white photographic images without direct human assistance. We explore var-ious network architectures, objectives, color .
Dual-domain Deep Convolutional Neural Networks for Image Demoireing An Gia Vien, Hyunkook Park, and Chul Lee Department of Multimedia Engineering Dongguk University, Seoul, Korea email@example.com, firstname.lastname@example.org, email@example.com Abstract We develop deep convolutional neural networks (CNNs)
Lecture: Deep Convolutional Neural Networks Shubhang Desai Stanford Vision and Learning Lab. s Stanford University 06-c-2018 2 Today’s agenda Deep convolutional networks . 28 28 3 Image 15 15 3 4 Filter 14 14 4 Output more output channels more filters more features we can learn! s Stanford University 06-c-
In the last two years, deep convolutional networks have outperformed the state of the art in many visual recognition tasks, e.g. [7,3]. While convolutional networks have already existed for a long time , their success was limited due to the size of the available training sets and the size of the considered networks. The
convolutional neural networks, transfer learning, automatization, microfossil identification, petrography ABSTRACT A convolutional neural network (CNN) is a deep learning (DL) method that has been widely and successfully applied to computer vision tasks including object localization, detection, and image classification.
2.3 Deep Convolutional Neural Networks A urry of recent results indicates that image de-scriptors extracted from deep convolutional neu-ral networks (CNNs) are very powerful and con-sistently outperform highly tuned state-of-the-art systems on a variety of visual recognition tasks (Razavian et al., 2014). Embeddings from state-
in deep convolutional neural networks, there is still ample room to further improve upon these results. 3. Methods 3.1. Convolutional neural networks A convolutional neural network (CNN) consists of layers that transform an input 3-dimensional volume into another output 3-dimensional volume through a differentiable func-tion.
Compact Deep Convolutional Neural Networks for Image Classiﬁcation Zejia Zheng, Zhu Li, Abhishek Nagar1 and Woosung Kang2 Abstract—Convolutional Neural Network is efﬁcient in learn-ing hierarchical features from large datasets, but its model complexity and large memory foot prints are preventing it from
via deep convolutional networks. This method shows remarkable detection accuracy on both the VOC and ImageNet datasets. But the feature computation in R-CNN is time-consuming, because it repeatedly applies the deep convolutional networks to the raw pixels of thousands of warped regions per image. In this paper, we show that we can run the .
Video Super-Resolution With Convolutional Neural Networks Armin Kappeler, Seunghwan Yoo, Qiqin Dai, and Aggelos K. Katsaggelos, Fellow, IEEE Abstract—Convolutional neural networks (CNN) are a special type of deep neural networks (DNN). They have so far been suc-cessfully applied to image super-resolution (SR) as well as other image .