867 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
Satellite remote sensing of surface winds, waves, and currents: Where are we now?
This review paper reports on the state-of-the-art concerning observations of surface winds, waves, and currents from space and their use for scientific research and subsequent applications. The development of observations of sea state parameters from space dates back to the 1970s, with a significant increase in the number and diversity of space missions since the 1990s. Sensors used to monitor the sea-state parameters from space are mainly based on microwave techniques. They are either specifically designed to monitor surface parameters or are used for their abilities to provide opportunistic measurements complementary to their primary purpose. The principles on which is based on the estimation of the sea surface parameters are first described, including the performance and limitations of each method. Numerous examples and references on the use of these observations for scientific and operational applications are then given. The richness and diversity of these applications are linked to the importance of knowledge of the sea state in many fields. Firstly, surface wind, waves, and currents are significant factors influencing exchanges at the air/sea interface, impacting oceanic and atmospheric boundary layers, contributing to sea level rise at the coasts, and interacting with the sea-ice formation or destruction in the polar zones. Secondly, ocean surface currents combined with wind- and wave- induced drift contribute to the transport of heat, salt, and pollutants. Waves and surface currents also impact sediment transport and erosion in coastal areas. For operational applications, observations of surface parameters are necessary on the one hand to constrain the numerical solutions of predictive models (numerical wave, oceanic, or atmospheric models), and on the other hand to validate their results. In turn, these predictive models are used to guarantee safe, efficient, and successful offshore operations, including the commercial shipping and energy sector, as well as tourism and coastal activities. Long-time series of global sea-state observations are also becoming increasingly important to analyze the impact of climate change on our environment. All these aspects are recalled in the article, relating to both historical and contemporary activities in these fields
Investigating Deep Learning Model Calibration for Classification Problems in Mechanics
Recently, there has been a growing interest in applying machine learning
methods to problems in engineering mechanics. In particular, there has been
significant interest in applying deep learning techniques to predicting the
mechanical behavior of heterogeneous materials and structures. Researchers have
shown that deep learning methods are able to effectively predict mechanical
behavior with low error for systems ranging from engineered composites, to
geometrically complex metamaterials, to heterogeneous biological tissue.
However, there has been comparatively little attention paid to deep learning
model calibration, i.e., the match between predicted probabilities of outcomes
and the true probabilities of outcomes. In this work, we perform a
comprehensive investigation into ML model calibration across seven open access
engineering mechanics datasets that cover three distinct types of mechanical
problems. Specifically, we evaluate both model and model calibration error for
multiple machine learning methods, and investigate the influence of ensemble
averaging and post hoc model calibration via temperature scaling. Overall, we
find that ensemble averaging of deep neural networks is both an effective and
consistent tool for improving model calibration, while temperature scaling has
comparatively limited benefits. Looking forward, we anticipate that this
investigation will lay the foundation for future work in developing mechanics
specific approaches to deep learning model calibration.Comment: 21 pages, 9 figure
2023-2024 Catalog
The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation
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