14 research outputs found
Learning three-dimensional flow for interactive aerodynamic design
We present a data-driven technique to instantly predict how fluid flows around various three-dimensional objects. Such simulation is useful for computational fabrication and engineering, but is usually computationally expensive since it requires solving the Navier-Stokes equation for many time steps. To accelerate the process, we propose a machine learning framework which predicts aerodynamic forces and velocity and pressure fields given a threedimensional shape input. Handling detailed free-form three-dimensional shapes in a data-driven framework is challenging because machine learning approaches usually require a consistent parametrization of input and output. We present a novel PolyCube maps-based parametrization that can be computed for three-dimensional shapes at interactive rates. This allows us to efficiently learn the nonlinear response of the flow using a Gaussian process regression. We demonstrate the effectiveness of our approach for the interactive design and optimization of a car body
Deep Fluids: A Generative Network for Parameterized Fluid Simulations
This paper presents a novel generative model to synthesize fluid simulations
from a set of reduced parameters. A convolutional neural network is trained on
a collection of discrete, parameterizable fluid simulation velocity fields. Due
to the capability of deep learning architectures to learn representative
features of the data, our generative model is able to accurately approximate
the training data set, while providing plausible interpolated in-betweens. The
proposed generative model is optimized for fluids by a novel loss function that
guarantees divergence-free velocity fields at all times. In addition, we
demonstrate that we can handle complex parameterizations in reduced spaces, and
advance simulations in time by integrating in the latent space with a second
network. Our method models a wide variety of fluid behaviors, thus enabling
applications such as fast construction of simulations, interpolation of fluids
with different parameters, time re-sampling, latent space simulations, and
compression of fluid simulation data. Reconstructed velocity fields are
generated up to 700x faster than re-simulating the data with the underlying CPU
solver, while achieving compression rates of up to 1300x.Comment: Computer Graphics Forum (Proceedings of EUROGRAPHICS 2019),
additional materials: http://www.byungsoo.me/project/deep-fluids
Deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields
Computational fluid dynamics (CFD) can be used to simulate vascular
haemodynamics and analyse potential treatment options. CFD has shown to be
beneficial in improving patient outcomes. However, the implementation of CFD
for routine clinical use is yet to be realised. Barriers for CFD include high
computational resources, specialist experience needed for designing simulation
set-ups, and long processing times. The aim of this study was to explore the
use of machine learning (ML) to replicate conventional aortic CFD with
automatic and fast regression models. Data used to train/test the model
consisted of 3,000 CFD simulations performed on synthetically generated 3D
aortic shapes. These subjects were generated from a statistical shape model
(SSM) built on real patient-specific aortas (N=67). Inference performed on 200
test shapes resulted in average errors of 6.01% +/-3.12 SD and 3.99% +/-0.93 SD
for pressure and velocity, respectively. Our ML-based models performed CFD in
+/-0.075 seconds (4,000x faster than the solver). This proof-of-concept study
shows that results from conventional vascular CFD can be reproduced using ML at
a much faster rate, in an automatic process, and with high accuracy.Comment: 22 pages, 19 figure
Part-Aware Product Design Agent Using Deep Generative Network and Local Linear Embedding
In this study, we present a data-driven generative design approach that can augment human creativity in product shape design with the objective of improving system performance. The approach consists of two modules: 1) a 3D mesh generative design module that can generate part-aware 3D objects using variational auto-encoder (VAE), and 2) a low-fidelity evaluation module that can rapidly assess the engineering performance of 3D objects based on locally linear embedding (LLE). This approach has two unique features. First, it generates 3D meshes that can better capture surface details (e.g., smoothness and curvature) given individual partsâ interconnection and constraints (i.e., part-aware), as opposed to generating holistic 3D shapes. Second, the LLE-based solver can assess the engineering performance of the generated 3D shapes to realize real-time evaluation. Our approach is applied to car design to reduce air drag for optimal aerodynamic performance
An augmented reality platform for interactive aerodynamic design and analysis
While modern CFD tools are able to provide the user with reliable and accurate simulations, there is a strong need for interactive design and analysis tools. State-of-the-art CFD software employs massive resources in terms of CPU time, user interaction, and also GPU time for rendering and analysis. In this work, we develop an innovative tool able to provide a seamless bridge between artistic design and engineering analysis. This platform has three main ingredients: computer vision to avoid long user interaction at the pre-processing stage, machine learning to avoid costly CFD simulations, and augmented reality for an agile and interactive post-processing of the results
Integrating Deep Learning into CAD/CAE System: Generative Design and Evaluation of 3D Conceptual Wheel
Engineering design research integrating artificial intelligence (AI) into
computer-aided design (CAD) and computer-aided engineering (CAE) is actively
being conducted. This study proposes a deep learning-based CAD/CAE framework in
the conceptual design phase that automatically generates 3D CAD designs and
evaluates their engineering performance. The proposed framework comprises seven
stages: (1) 2D generative design, (2) dimensionality reduction, (3) design of
experiment in latent space, (4) CAD automation, (5) CAE automation, (6)
transfer learning, and (7) visualization and analysis. The proposed framework
is demonstrated through a road wheel design case study and indicates that AI
can be practically incorporated into an end-use product design project.
Engineers and industrial designers can jointly review a large number of
generated 3D CAD models by using this framework along with the engineering
performance results estimated by AI and find conceptual design candidates for
the subsequent detailed design stage
DMIS: Dynamic Mesh-based Importance Sampling for Training Physics-Informed Neural Networks
Modeling dynamics in the form of partial differential equations (PDEs) is an
effectual way to understand real-world physics processes. For complex physics
systems, analytical solutions are not available and numerical solutions are
widely-used. However, traditional numerical algorithms are computationally
expensive and challenging in handling multiphysics systems. Recently, using
neural networks to solve PDEs has made significant progress, called
physics-informed neural networks (PINNs). PINNs encode physical laws into
neural networks and learn the continuous solutions of PDEs. For the training of
PINNs, existing methods suffer from the problems of inefficiency and unstable
convergence, since the PDE residuals require calculating automatic
differentiation. In this paper, we propose Dynamic Mesh-based Importance
Sampling (DMIS) to tackle these problems. DMIS is a novel sampling scheme based
on importance sampling, which constructs a dynamic triangular mesh to estimate
sample weights efficiently. DMIS has broad applicability and can be easily
integrated into existing methods. The evaluation of DMIS on three widely-used
benchmarks shows that DMIS improves the convergence speed and accuracy in the
meantime. Especially in solving the highly nonlinear Schr\"odinger Equation,
compared with state-of-the-art methods, DMIS shows up to 46% smaller root mean
square error and five times faster convergence speed. Code are available at
https://github.com/MatrixBrain/DMIS.Comment: Accepted to AAAl-2
Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields
Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is yet to be realised. Barriers for CFD include high computational resources, specialist experience needed for designing simulation set-ups, and long processing times. The aim of this study was to explore the use of machine learning (ML) to replicate conventional aortic CFD with automatic and fast regression models. Data used to train/test the model consisted of 3,000 CFD simulations performed on synthetically generated 3D aortic shapes. These subjects were generated from a statistical shape model (SSM) built on real patient-specific aortas (N = 67). Inference performed on 200 test shapes resulted in average errors of 6.01% ±3.12 SD and 3.99% ±0.93 SD for pressure and velocity, respectively. Our ML-based models performed CFD in âŒ0.075 seconds (4,000x faster than the solver). This proof-of-concept study shows that results from conventional vascular CFD can be reproduced using ML at a much faster rate, in an automatic process, and with reasonable accuracy
An augmented reality platform for interactive aerodynamic design and analysis
While modern CFD tools are able to provide the user with reliable and accurate simulations, there is a strong need for interactive design and analysis tools. State-of-the-art CFD software employs massive resources in terms of CPU time, user interaction, and also GPU time for rendering and analysis. In this work, we develop an innovative tool able to provide a seamless bridge between artistic design and engineering analysis. This platform has three main ingredients: computer vision to avoid long user interaction at the pre-processing stage, machine learning to avoid costly CFD simulations, and augmented reality for an agile and interactive post-processing of the results
Computational Design of Cold Bent Glass Fa\c{c}ades
Cold bent glass is a promising and cost-efficient method for realizing doubly
curved glass fa\c{c}ades. They are produced by attaching planar glass sheets to
curved frames and require keeping the occurring stress within safe limits.
However, it is very challenging to navigate the design space of cold bent glass
panels due to the fragility of the material, which impedes the form-finding for
practically feasible and aesthetically pleasing cold bent glass fa\c{c}ades. We
propose an interactive, data-driven approach for designing cold bent glass
fa\c{c}ades that can be seamlessly integrated into a typical architectural
design pipeline. Our method allows non-expert users to interactively edit a
parametric surface while providing real-time feedback on the deformed shape and
maximum stress of cold bent glass panels. Designs are automatically refined to
minimize several fairness criteria while maximal stresses are kept within glass
limits. We achieve interactive frame rates by using a differentiable Mixture
Density Network trained from more than a million simulations. Given a curved
boundary, our regression model is capable of handling multistable
configurations and accurately predicting the equilibrium shape of the panel and
its corresponding maximal stress. We show predictions are highly accurate and
validate our results with a physical realization of a cold bent glass surface