12,579 research outputs found
Enhanced Input Modeling for Construction Simulation using Bayesian Deep Neural Networks
This paper aims to propose a novel deep learning-integrated framework for
deriving reliable simulation input models through incorporating multi-source
information. The framework sources and extracts multisource data generated from
construction operations, which provides rich information for input modeling.
The framework implements Bayesian deep neural networks to facilitate the
purpose of incorporating richer information in input modeling. A case study on
road paving operation is performed to test the feasibility and applicability of
the proposed framework. Overall, this research enhances input modeling by
deriving detailed input models, thereby, augmenting the decision-making
processes in construction operations. This research also sheds lights on
prompting data-driven simulation through incorporating machine learning
techniques
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
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