8,737 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
Teaching an Active Learner with Contrastive Examples
We study the problem of active learning with the added twist that the learner
is assisted by a helpful teacher. We consider the following natural interaction
protocol: At each round, the learner proposes a query asking for the label of
an instance , the teacher provides the requested label
along with explanatory information to guide the learning process. In this
paper, we view this information in the form of an additional contrastive
example () where is picked from a set constrained by
(e.g., dissimilar instances with the same label). Our focus is to design a
teaching algorithm that can provide an informative sequence of contrastive
examples to the learner to speed up the learning process. We show that this
leads to a challenging sequence optimization problem where the algorithm's
choices at a given round depend on the history of interactions. We investigate
an efficient teaching algorithm that adaptively picks these contrastive
examples. We derive strong performance guarantees for our algorithm based on
two problem-dependent parameters and further show that for specific types of
active learners (e.g., a generalized binary search learner), the proposed
teaching algorithm exhibits strong approximation guarantees. Finally, we
illustrate our bounds and demonstrate the effectiveness of our teaching
framework via two numerical case studies.Comment: Fix the illustrative exampl
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