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Informing Neural Networks with Simplified Physics for Better Flow Prediction

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Railway demand forecasting in revenue management using neural networks

Shadi Sharif Azadeh

This study analyses the use of neural networks to produce accurate forecasts of total bookings and cancellations before departure, of a major European rail operator. Effective forecasting models, can improve revenue performance of transportation companies ...

2013

Forced and Self Oscillations of Hydraulic Systems Induced by Cavitation Vortex Rope of Francis Turbines

Sébastien Alligné

With economical energy market strategies based on instantaneous pricings of electricity as function of the demand or the predictions, operators harness more hydroelectric facilities to off-design operating points to cover the variations of the electricity ...

EPFL2011

The research work reported in the present dissertation is aimed at the analysis of complex physical phenomena involving instabilities and nonlinearities occurring in fluids through state-of-the-art numerical modeling. Solutions of intricate fluid physics p ...

EPFL2007

Spectral methods, particularly in their multidomain version, have become firmly established as a mainstream tool for scientific and engineering computation. While retaining the tight integration between the theoretical and practical aspects of spectral met ...

Springer2007

Computational fluid dynamics for naval engineering problems

Nicola Parolini

The subject of this thesis is the numerical simulation of viscous free-surface flows in naval engineering applications. State-of-the-art numerical methods based on the solution of the Navier-Stokes equations are used to predict the flow around different cl ...

EPFL2004

Numerical simulation using RANS-based tools for America's Cup design

Mark Sawley, Nicola Parolini

The application of Computational Fluid Dynamics simulations based on the Reynolds Averaged Navier- Stokes (RANS) equations to the design of sailing yachts is becoming more commonplace, particularly for the America's Cup. Drawing on the experience of the Ec ...

2003

Real-time computing without stable states: a new framework for neural computation based on perturbations

Henry Markram

A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real time. We propose a new computationa ...

2002

We will discuss in this survey article a new framework for analysing computations on time series and in particular on spike trains, introduced in (Maass et. al. 2002). In contrast to common computational models this new framework does not require that info ...

2002

Parallel computation and analysis of the flow in a static mixer

Mark Sawley

The use of a high-performance parallel computer system has been investigated to compute the incompressible flow and mixing efficiency of a static mixer. The flow computation is performed using a conventional Eulerian approach, by resolving the governing fl ...

1999

High-performance parallel computing for incompressible flow simulations

Mark Sawley, Michel Deville

High-performance parallel computer systems have been employed to compute a variety of three-dimensional incompressible fluid flows. Three different numerical methods have been used for the discretization of the Navier-Stokes equations, with domain decompos ...

1999