1,670 research outputs found
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation
Turbulent flows have historically presented formidable challenges to
predictive computational modeling. Traditional numerical simulations often
require vast computational resources, making them infeasible for numerous
engineering applications. As an alternative, deep learning-based surrogate
models have emerged, offering data-drive solutions. However, these are
typically constructed within deterministic settings, leading to shortfall in
capturing the innate chaotic and stochastic behaviors of turbulent dynamics. We
introduce a novel generative framework grounded in probabilistic diffusion
models for versatile generation of spatiotemporal turbulence. Our method
unifies both unconditional and conditional sampling strategies within a
Bayesian framework, which can accommodate diverse conditioning scenarios,
including those with a direct differentiable link between specified conditions
and generated unsteady flow outcomes, and scenarios lacking such explicit
correlations. A notable feature of our approach is the method proposed for
long-span flow sequence generation, which is based on autoregressive
gradient-based conditional sampling, eliminating the need for cumbersome
retraining processes. We showcase the versatile turbulence generation
capability of our framework through a suite of numerical experiments,
including: 1) the synthesis of LES simulated instantaneous flow sequences from
URANS inputs; 2) holistic generation of inhomogeneous, anisotropic wall-bounded
turbulence, whether from given initial conditions, prescribed turbulence
statistics, or entirely from scratch; 3) super-resolved generation of
high-speed turbulent boundary layer flows from low-resolution data across a
range of input resolutions. Collectively, our numerical experiments highlight
the merit and transformative potential of the proposed methods, making a
significant advance in the field of turbulence generation.Comment: 37 pages, 31 figure
Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems
Simulating complex physical systems often involves solving partial differential equations (PDEs) with some closures due to the presence of multi-scale physics that cannot be fully resolved. Although the advancement of high performance computing has made resolving small-scale physics possible, such simulations are still very expensive. Therefore, reliable and accurate closure models for the unresolved physics remains an important requirement for many computational physics problems, e.g., turbulence simulation. Recently, several researchers have adopted generative adversarial networks (GANs), a novel paradigm of training machine learning models, to generate solutions of PDEs-governed complex systems without having to numerically solve these PDEs. However, GANs are known to be difficult in training and likely to converge to local minima, where the generated samples do not capture the true statistics of the training data. In this work, we present a statistical constrained generative adversarial network by enforcing constraints of covariance from the training data, which results in an improved machine-learning-based emulator to capture the statistics of the training data generated by solving fully resolved PDEs. We show that such a statistical regularization leads to better performance compared to standard GANs, measured by (1) the constrained model's ability to more faithfully emulate certain physical properties of the system and (2) the significantly reduced (by up to 80%) training time to reach the solution. We exemplify this approach on the Rayleigh-Bénard convection, a turbulent flow system that is an idealized model of the Earth's atmosphere. With the growth of high-fidelity simulation databases of physical systems, this work suggests great potential for being an alternative to the explicit modeling of closures or parameterizations for unresolved physics, which are known to be a major source of uncertainty in simulating multi-scale physical systems, e.g., turbulence or Earth's climate
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
Physics-Informed Computer Vision: A Review and Perspectives
Incorporation of physical information in machine learning frameworks are
opening and transforming many application domains. Here the learning process is
augmented through the induction of fundamental knowledge and governing physical
laws. In this work we explore their utility for computer vision tasks in
interpreting and understanding visual data. We present a systematic literature
review of formulation and approaches to computer vision tasks guided by
physical laws. We begin by decomposing the popular computer vision pipeline
into a taxonomy of stages and investigate approaches to incorporate governing
physical equations in each stage. Existing approaches in each task are analyzed
with regard to what governing physical processes are modeled, formulated and
how they are incorporated, i.e. modify data (observation bias), modify networks
(inductive bias), and modify losses (learning bias). The taxonomy offers a
unified view of the application of the physics-informed capability,
highlighting where physics-informed learning has been conducted and where the
gaps and opportunities are. Finally, we highlight open problems and challenges
to inform future research. While still in its early days, the study of
physics-informed computer vision has the promise to develop better computer
vision models that can improve physical plausibility, accuracy, data efficiency
and generalization in increasingly realistic applications
Multi-fidelity information fusion with concatenated neural networks
Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.publishedVersio
- …