506 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
Stateâofâtheâart report on nonlinear representation of sources and channels
This report consists of two complementary parts, related to the modeling of two important sources of nonlinearities in a communications system. In the first part, an overview of important past work related to the estimation, compression and processing of sparse data through the use of nonlinear models is provided. In the second part, the current state of the art on the representation of wireless channels in the presence of nonlinearities is summarized. In addition to the characteristics of the nonlinear wireless fading channel, some information is also provided on recent approaches to the sparse representation of such channels
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How
With the ever-increasing number of pretrained models, machine learning
practitioners are continuously faced with which pretrained model to use, and
how to finetune it for a new dataset. In this paper, we propose a methodology
that jointly searches for the optimal pretrained model and the hyperparameters
for finetuning it. Our method transfers knowledge about the performance of many
pretrained models with multiple hyperparameter configurations on a series of
datasets. To this aim, we evaluated over 20k hyperparameter configurations for
finetuning 24 pretrained image classification models on 87 datasets to generate
a large-scale meta-dataset. We meta-learn a multi-fidelity performance
predictor on the learning curves of this meta-dataset and use it for fast
hyperparameter optimization on new datasets. We empirically demonstrate that
our resulting approach can quickly select an accurate pretrained model for a
new dataset together with its optimal hyperparameters
Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?
Learning decompositions of expensive-to-evaluate black-box functions promises
to scale Bayesian optimisation (BO) to high-dimensional problems. However, the
success of these techniques depends on finding proper decompositions that
accurately represent the black-box. While previous works learn those
decompositions based on data, we investigate data-independent decomposition
sampling rules in this paper. We find that data-driven learners of
decompositions can be easily misled towards local decompositions that do not
hold globally across the search space. Then, we formally show that a random
tree-based decomposition sampler exhibits favourable theoretical guarantees
that effectively trade off maximal information gain and functional mismatch
between the actual black-box and its surrogate as provided by the
decomposition. Those results motivate the development of the random
decomposition upper-confidence bound algorithm (RDUCB) that is straightforward
to implement - (almost) plug-and-play - and, surprisingly, yields significant
empirical gains compared to the previous state-of-the-art on a comprehensive
set of benchmarks. We also confirm the plug-and-play nature of our modelling
component by integrating our method with HEBO, showing improved practical gains
in the highest dimensional tasks from Bayesmark
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