21,902 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
Generalised additive multiscale wavelet models constructed using particle swarm optimisation and mutual information for spatio-temporal evolutionary system representation
A new class of generalised additive multiscale wavelet models (GAMWMs) is introduced for high dimensional spatio-temporal evolutionary (STE) system identification. A novel two-stage hybrid learning scheme is developed for constructing such an additive wavelet model. In the first stage, a new orthogonal projection pursuit (OPP) method, implemented using a particle swarm optimisation(PSO) algorithm, is proposed for successively augmenting an initial coarse wavelet model, where relevant parameters of the associated wavelets are optimised using a particle swarm optimiser. The resultant network model, obtained in the first stage, may however be a redundant model. In the second stage, a forward orthogonal regression (FOR) algorithm, implemented using a mutual information method, is then applied to refine and improve the initially constructed wavelet model. The proposed two-stage hybrid method can generally produce a parsimonious wavelet model, where a ranked list of wavelet functions, according to the capability of each wavelet to represent the total variance in the desired system output signal is produced. The proposed new modelling framework is applied to real observed images, relative to a chemical reaction exhibiting a spatio-temporal evolutionary behaviour, and the associated identification results show that the new modelling framework is applicable and effective for handling high dimensional identification problems of spatio-temporal evolution sytems
Local thermal energy as a structural indicator in glasses
Identifying heterogeneous structures in glasses --- such as localized soft
spots --- and understanding structure-dynamics relations in these systems
remain major scientific challenges. Here we derive an exact expression for the
local thermal energy of interacting particles (the mean local potential energy
change due to thermal fluctuations) in glassy systems by a systematic
low-temperature expansion. We show that the local thermal energy can attain
anomalously large values, inversely related to the degree of softness of
localized structures in a glass, determined by a coupling between internal
stresses --- an intrinsic signature of glassy frustration ---, anharmonicity
and low-frequency vibrational modes. These anomalously large values follow a
fat-tailed distribution, with a universal exponent related to the recently
observed universal density of states of quasi-localized
low-frequency vibrational modes. When the spatial thermal energy field --- a
`softness field' --- is considered, this power-law tail manifests itself by
highly localized spots which are significantly softer than their surroundings.
These soft spots are shown to be susceptible to plastic rearrangements under
external driving forces, having predictive powers that surpass those of the
normal-modes-based approach. These results offer a general,
system/model-independent, physical-observable-based approach to identify
structural properties of quiescent glasses and to relate them to glassy
dynamics.Comment: 8 pages, 4 figures + Supporting Information, shorter title, minor
textual change
A Parallel Iterative Method for Computing Molecular Absorption Spectra
We describe a fast parallel iterative method for computing molecular
absorption spectra within TDDFT linear response and using the LCAO method. We
use a local basis of "dominant products" to parametrize the space of orbital
products that occur in the LCAO approach. In this basis, the dynamical
polarizability is computed iteratively within an appropriate Krylov subspace.
The iterative procedure uses a a matrix-free GMRES method to determine the
(interacting) density response. The resulting code is about one order of
magnitude faster than our previous full-matrix method. This acceleration makes
the speed of our TDDFT code comparable with codes based on Casida's equation.
The implementation of our method uses hybrid MPI and OpenMP parallelization in
which load balancing and memory access are optimized. To validate our approach
and to establish benchmarks, we compute spectra of large molecules on various
types of parallel machines.
The methods developed here are fairly general and we believe they will find
useful applications in molecular physics/chemistry, even for problems that are
beyond TDDFT, such as organic semiconductors, particularly in photovoltaics.Comment: 20 pages, 17 figures, 3 table
Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables
Understanding nonlinear dynamical systems (NLDSs) is challenging in a variety
of engineering and scientific fields. Dynamic mode decomposition (DMD), which
is a numerical algorithm for the spectral analysis of Koopman operators, has
been attracting attention as a way of obtaining global modal descriptions of
NLDSs without requiring explicit prior knowledge. However, since existing DMD
algorithms are in principle formulated based on the concatenation of scalar
observables, it is not directly applicable to data with dependent structures
among observables, which take, for example, the form of a sequence of graphs.
In this paper, we formulate Koopman spectral analysis for NLDSs with structures
among observables and propose an estimation algorithm for this problem. This
method can extract and visualize the underlying low-dimensional global dynamics
of NLDSs with structures among observables from data, which can be useful in
understanding the underlying dynamics of such NLDSs. To this end, we first
formulate the problem of estimating spectra of the Koopman operator defined in
vector-valued reproducing kernel Hilbert spaces, and then develop an estimation
procedure for this problem by reformulating tensor-based DMD. As a special case
of our method, we propose the method named as Graph DMD, which is a numerical
algorithm for Koopman spectral analysis of graph dynamical systems, using a
sequence of adjacency matrices. We investigate the empirical performance of our
method by using synthetic and real-world data.Comment: 34 pages with 4 figures, Published in Neural Networks, 201
A machine learning study to identify spinodal clumping in high energy nuclear collisions
The coordinate and momentum space configurations of the net baryon number in heavy ion collisions that undergo spinodal decomposition, due to a first-order phase transition, are investigated using state-of-the-art machine-learning methods. Coordinate space clumping, which appears in the spinodal decomposition, leaves strong characteristic imprints on the spatial net density distribution in nearly every event which can be detected by modern machine learning techniques. On the other hand, the corresponding features in the momentum distributions cannot clearly be detected, by the same machine learning methods, in individual events. Only a small subset of events can be systematically differ- entiated if only the momentum space information is available. This is due to the strong similarity of the two event classes, with and without spinodal decomposition. In such sce- narios, conventional event-averaged observables like the baryon number cumulants signal a spinodal non-equilibrium phase transition. Indeed the third-order cumulant, the skewness, does exhibit a peak at the beam energy (Elab = 3–4 A GeV), where the transient hot and dense system created in the heavy ion collision reaches the first-order phase transition
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