4,231 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
The Unsupervised Acquisition of a Lexicon from Continuous Speech
We present an unsupervised learning algorithm that acquires a
natural-language lexicon from raw speech. The algorithm is based on the optimal
encoding of symbol sequences in an MDL framework, and uses a hierarchical
representation of language that overcomes many of the problems that have
stymied previous grammar-induction procedures. The forward mapping from symbol
sequences to the speech stream is modeled using features based on articulatory
gestures. We present results on the acquisition of lexicons and language models
from raw speech, text, and phonetic transcripts, and demonstrate that our
algorithm compares very favorably to other reported results with respect to
segmentation performance and statistical efficiency.Comment: 27 page technical repor
A Proposal on Discovering Causal Structures inTechnical Systems by Means of Interventions
Causal Discovery has become an area of high interest for researchers. It haslead to great advances in medicine, in the social sciences and in genetics. Butup til now, it is hardly used to identify causal relations in technical systems.This paper presents the basic building blocks for in-depth research. This paperreviews established causal discovery methods and causal models. In contrast toexisting surveys of this domain, we focus on the causal discovery methods usinginterventions. Based thereon, we propose the idea of a promising interventionaldiscovery approach for technical systems. It takes advantage of not only direct,but also indirect causal relationships, which might improve the learning processof causal structures
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