606 research outputs found
An embedding technique for the solution of reaction-fiffusion equations on algebraic surfaces with isolated singularities
In this paper we construct a parametrization-free embedding technique for numerically evolving reaction-diffusion PDEs defined on algebraic curves that possess an isolated singularity. In our approach, we first desingularize the curve by appealing to techniques from algebraic geometry.\ud
We create a family of smooth curves in higher dimensional space that correspond to the original curve by projection. Following this, we pose the analogous reaction-diffusion PDE on each member of this family and show that the solutions (their projection onto the original domain) approximate the solution of the original problem. Finally, we compute these approximants numerically by applying the Closest Point Method which is an embedding technique for solving PDEs on smooth surfaces of arbitrary dimension or codimension, and is thus suitable for our situation. In addition, we discuss the potential to generalize the techniques presented for higher-dimensional surfaces with multiple singularities
Time Domain Simulations of EMRIs using Finite Element Methods
This is a brief report on time-domain numerical simulations of
extreme-mass-ratio binaries based on finite element methods. We discuss a new
technique for solving the perturbative equations describing a point-like object
orbiting a non-rotating massive black hole and the prospects of using it for
the evaluation of the gravitational self-force responsible of the inspiral of
these binary systems. We also discuss the perspectives of transferring this
technology to the more astrophysically relevant case of a central rotating
massive black hole.Comment: 5 pages. Submitted to the proceedings of the 6th LISA symposiu
Approximation of Bayesian inverse problems for PDEs
Inverse problems are often ill posed, with solutions that depend sensitively on data. In any numerical approach to the solution of such problems, regularization of some form is needed to counteract the resulting instability. This paper is based on an approach to regularization, employing a Bayesian formulation of the problem, which leads to a notion of well posedness for inverse problems, at the level of probability measures. The stability which results from this well posedness may be used as the basis for quantifying the approximation, in finite dimensional spaces, of inverse problems for functions. This paper contains a theory which utilizes this stability property to estimate the distance between the true and approximate posterior distributions, in the Hellinger metric, in terms of error estimates for approximation of the underlying forward problem. This is potentially useful as it allows for the transfer of estimates from the numerical analysis of forward problems into estimates for the solution of the related inverse problem. It is noteworthy that, when the prior is a Gaussian random field model, controlling differences in the Hellinger metric leads to control on the differences between expected values of polynomially bounded functions and operators, including the mean and covariance operator. The ideas are applied to some non-Gaussian inverse problems where the goal is determination of the initial condition for the Stokes or NavierāStokes equation from Lagrangian and Eulerian observations, respectively
Parameterization adaption for 3D shape optimization in aerodynamics
When solving a PDE problem numerically, a certain mesh-refinement process is
always implicit, and very classically, mesh adaptivity is a very effective
means to accelerate grid convergence. Similarly, when optimizing a shape by
means of an explicit geometrical representation, it is natural to seek for an
analogous concept of parameterization adaptivity. We propose here an adaptive
parameterization for three-dimensional optimum design in aerodynamics by using
the so-called "Free-Form Deformation" approach based on 3D tensorial B\'ezier
parameterization. The proposed procedure leads to efficient numerical
simulations with highly reduced computational costs
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