You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
The Matter of History links the history of people with the history of things through a bold new materialist theory of the past.
Bluff-body wakes play an important role in many fluid dynamics problems and engineering applications. This book gives and up-to-date account of recent results obtained in the study of bluff-body wakes. Experimental, theoretical and numerical approaches are all comprehensively covered and compared. Topics of particular interest include hydrodynamic instability analyses, three-dimensional pattern formation problems, flow control methods, bifurcation analyses, numerical simulations and turbulence modelling. The main originality of thisvolume is that recent conceptual advances made to describe nonlinear phenomena in general are put to the test on a classical problem in fundamental fluid mechanics, namely the wake structure generated behind a bluff object.
Drawing from experts and top researchers from around the world, this book presents current developments in a variety of areas that impact offshore and ocean engineering.
System Theory: Modeling, Analysis and Control contains thirty-three scientific papers covering a wide range of topics in systems and control. These papers have been contributed to a symposium organized to celebrate Sanjoy K. Mitter's 65th birthday. The following research topics are addressed: distributed parameter systems, stochastic control, filtering and estimation, optimization and optimal control, image processing and vision, hierarchical systems and hybrid control, nonlinear systems, and linear systems. Also included are three survey papers on optimization, nonlinear filtering, and nonlinear systems. Recent advances are reported on the behavioral approach to systems, the relationship be...
Drawing from experts and top researchers from around the world, this book presents current developments in a variety of areas that impact offshore and ocean engineering.
Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and d...
None
None