10 research outputs found
Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models
Modeling is crucial to understanding the effect of greenhouse gases, warming,
and ice sheet melting on the ocean. At the same time, ocean processes affect
phenomena such as hurricanes and droughts. Parameters in the models that cannot
be physically measured have a significant effect on the model output. For an
idealized ocean model, we generated perturbed parameter ensemble data and
trained surrogate neural network models. The neural surrogates accurately
predicted the one-step forward dynamics, of which we then computed the
parametric sensitivity
Quantum Algorithm Implementations for Beginners
As quantum computers become available to the general public, the need has
arisen to train a cohort of quantum programmers, many of whom have been
developing classical computer programs for most of their careers. While
currently available quantum computers have less than 100 qubits, quantum
computing hardware is widely expected to grow in terms of qubit count, quality,
and connectivity. This review aims to explain the principles of quantum
programming, which are quite different from classical programming, with
straightforward algebra that makes understanding of the underlying fascinating
quantum mechanical principles optional. We give an introduction to quantum
computing algorithms and their implementation on real quantum hardware. We
survey 20 different quantum algorithms, attempting to describe each in a
succinct and self-contained fashion. We show how these algorithms can be
implemented on IBM's quantum computer, and in each case, we discuss the results
of the implementation with respect to differences between the simulator and the
actual hardware runs. This article introduces computer scientists, physicists,
and engineers to quantum algorithms and provides a blueprint for their
implementations
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Enhanced inverse-cascade of energy in the averaged Euler equations
For a particular choice of the smoothing kernel, it is shown that the system of
partial differential equations governing the vortex-blob method corresponds to the averaged
Euler equations. These latter equations have recently been derived by averaging the Euler
equations over Lagrangian fluctuations of length scale \a, and the same system is also
encountered in the description of inviscid and incompressible flow of second-grade
polymeric (non-Newtonian) fluids. While previous studies of this system have noted the
suppression of nonlinear interaction between modes smaller than \a, we show that the
modification of the nonlinear advection term also acts to enhance the inverse-cascade of
energy in two-dimensional turbulence and thereby affects scales of motion larger than \a
as well. This latter effect is reminiscent of the drag-reduction that occurs in a turbulent
flow when a dilute polymer is added
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On the Viability of Quantum Annealers to Solve Fluid Flows
This paper explores the suitability of upcoming novel computing technologies, particularly adiabatic annealing based quantum computers, to solve fluid dynamics problems that form a critical component of several science and engineering applications. For our experiments, we start with a well-studied one-dimensional simple flow problem, and provide a framework to convert such problems in continuum to a form amenable for deployment on such quantum annealers. Since the DWave annealer returns multiple states sampling the energy landscape of the problem, we explore multiple solution selection strategies to approximate the solution of the problem. We analyze the continuum solutions obtained both qualitatively and quantitatively as well as their sensitivities to the particular solution selection scheme