43 research outputs found
Optimal-order isogeometric collocation at Galerkin superconvergent points
In this paper we investigate numerically the order of convergence of an
isogeometric collocation method that builds upon the least-squares collocation
method presented in [1] and the variational collocation method presented in
[2]. The focus is on smoothest B-splines/NURBS approximations, i.e, having
global continuity for polynomial degree . Within the framework of
[2], we select as collocation points a subset of those considered in [1], which
are related to the Galerkin superconvergence theory. With our choice, that
features local symmetry of the collocation stencil, we improve the convergence
behaviour with respect to [2], achieving optimal -convergence for odd
degree B-splines/NURBS approximations. The same optimal order of convergence is
seen in [1], where, however a least-squares formulation is adopted. Further
careful study is needed, since the robustness of the method and its
mathematical foundation are still unclear.Comment: 21 pages, 20 figures (35 pdf images
A sparse-grid isogeometric solver
Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS
as a basis for the approximation of the solution of PDEs. In this work, we
investigate to which extent IGA solvers can benefit from the so-called
sparse-grids construction in its combination technique form, which was first
introduced in the early 90s in the context of the approximation of
high-dimensional PDEs. The tests that we report show that, in accordance to the
literature, a sparse-grid construction can indeed be useful if the solution of
the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the
case of non-smooth solutions when some a-priori knowledge on the location of
the singularities of the solution can be exploited to devise suitable
non-equispaced meshes. Finally, we remark that sparse grids can be seen as a
simple way to parallelize pre-existing serial IGA solvers in a straightforward
fashion, which can be beneficial in many practical situations.Comment: updated version after revie
IGA-based Multi-Index Stochastic Collocation for random PDEs on arbitrary domains
This paper proposes an extension of the Multi-Index Stochastic Collocation
(MISC) method for forward uncertainty quantification (UQ) problems in
computational domains of shape other than a square or cube, by exploiting
isogeometric analysis (IGA) techniques. Introducing IGA solvers to the MISC
algorithm is very natural since they are tensor-based PDE solvers, which are
precisely what is required by the MISC machinery. Moreover, the
combination-technique formulation of MISC allows the straight-forward reuse of
existing implementations of IGA solvers. We present numerical results to
showcase the effectiveness of the proposed approach.Comment: version 3, version after revisio
Uncertainty Quantification of geochemical and mechanical compaction in layered sedimentary basins
In this work we propose an Uncertainty Quantification methodology for
sedimentary basins evolution under mechanical and geochemical compaction
processes, which we model as a coupled, time-dependent, non-linear,
monodimensional (depth-only) system of PDEs with uncertain parameters. While in
previous works (Formaggia et al. 2013, Porta et al., 2014) we assumed a
simplified depositional history with only one material, in this work we
consider multi-layered basins, in which each layer is characterized by a
different material, and hence by different properties. This setting requires
several improvements with respect to our earlier works, both concerning the
deterministic solver and the stochastic discretization. On the deterministic
side, we replace the previous fixed-point iterative solver with a more
efficient Newton solver at each step of the time-discretization. On the
stochastic side, the multi-layered structure gives rise to discontinuities in
the dependence of the state variables on the uncertain parameters, that need an
appropriate treatment for surrogate modeling techniques, such as sparse grids,
to be effective. We propose an innovative methodology to this end which relies
on a change of coordinate system to align the discontinuities of the target
function within the random parameter space. The reference coordinate system is
built upon exploiting physical features of the problem at hand. We employ the
locations of material interfaces, which display a smooth dependence on the
random parameters and are therefore amenable to sparse grid polynomial
approximations. We showcase the capabilities of our numerical methodologies
through two synthetic test cases. In particular, we show that our methodology
reproduces with high accuracy multi-modal probability density functions
displayed by target state variables (e.g., porosity).Comment: 25 pages, 30 figure
Multi-index Stochastic Collocation convergence rates for random PDEs with parametric regularity
We analyze the recent Multi-index Stochastic Collocation (MISC) method for
computing statistics of the solution of a partial differential equation (PDEs)
with random data, where the random coefficient is parametrized by means of a
countable sequence of terms in a suitable expansion. MISC is a combination
technique based on mixed differences of spatial approximations and quadratures
over the space of random data and, naturally, the error analysis uses the joint
regularity of the solution with respect to both the variables in the physical
domain and parametric variables. In MISC, the number of problem solutions
performed at each discretization level is not determined by balancing the
spatial and stochastic components of the error, but rather by suitably
extending the knapsack-problem approach employed in the construction of the
quasi-optimal sparse-grids and Multi-index Monte Carlo methods. We use a greedy
optimization procedure to select the most effective mixed differences to
include in the MISC estimator. We apply our theoretical estimates to a linear
elliptic PDEs in which the log-diffusion coefficient is modeled as a random
field, with a covariance similar to a Mat\'ern model, whose realizations have
spatial regularity determined by a scalar parameter. We conduct a complexity
analysis based on a summability argument showing algebraic rates of convergence
with respect to the overall computational work. The rate of convergence depends
on the smoothness parameter, the physical dimensionality and the efficiency of
the linear solver. Numerical experiments show the effectiveness of MISC in this
infinite-dimensional setting compared with the Multi-index Monte Carlo method
and compare the convergence rate against the rates predicted in our theoretical
analysis
Uncertainty quantification in timber-like beams using sparse grids: theory and examples with off-the-shelf software utilization
When dealing with timber structures, the characteristic strength and
stiffness of the material are made highly variable and uncertain by the
unavoidable, yet hardly predictable, presence of knots and other defects. In
this work we apply the sparse grids stochastic collocation method to perform
uncertainty quantification for structural engineering in the scenario described
above. Sparse grids have been developed by the mathematical community in the
last decades and their theoretical background has been rigorously and
extensively studied. The document proposes a brief practice-oriented
introduction with minimal theoretical background, provides detailed
instructions for the use of the already implemented Sparse Grid Matlab kit
(freely available on-line) and discusses two numerical examples inspired from
timber engineering problems that highlight how sparse grids exhibit superior
performances compared to the plain Monte Carlo method. The Sparse Grid Matlab
kit requires only a few lines of code to be interfaced with any numerical
solver for mechanical problems (in this work we used an isogeometric
collocation method) and provides outputs that can be easily interpreted and
used in the engineering practice
A note on tools for prediction under uncertainty and identifiability of SIR-like dynamical systems for epidemiology
We provide an overview of the methods that can be used for prediction under
uncertainty and data fitting of dynamical systems, and of the fundamental
challenges that arise in this context. The focus is on SIR-like models, that
are being commonly used when attempting to predict the trend of the COVID-19
pandemic. In particular, we raise a warning flag about identifiability of the
parameters of SIR-like models; often, it might be hard to infer the correct
values of the parameters from data, even for very simple models, making it
non-trivial to use these models for meaningful predictions. Most of the points
that we touch upon are actually generally valid for inverse problems in more
general setups
On the convergence of adaptive stochastic collocation for elliptic partial differential equations with affine diffusion
Convergence of an adaptive collocation method for the stationary parametric diffusion equation with finite-dimensional affine coefficient is shown. The adaptive algorithm relies on a recently introduced residual-based reliable a posteriori error estimator. For the convergence proof, a strategy recently used for a stochastic Galerkin method with an hierarchical error estimator is transferred to the collocation setting