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      Multiscale modeling and simulation of brain blood flow

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      Physics of Fluids

      AIP Publishing

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          Most cited references 67

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          Statistical Mechanics of Dissipative Particle Dynamics

           P Español,  D Warren (1995)
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            Probabilistic machine learning and artificial intelligence.

            How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.
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              High-order splitting methods for the incompressible Navier-Stokes equations

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                Author and article information

                Journal
                Physics of Fluids
                Physics of Fluids
                AIP Publishing
                1070-6631
                1089-7666
                February 2016
                February 2016
                : 28
                : 2
                : 021304
                Article
                10.1063/1.4941315
                © 2016
                Product

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