A Finite Element Method With Singularity Reconstruction for Fractional Boundary Value Problems

We consider a two-point boundary value problem involving a Riemann-Liouville fractional derivative of order \al\in (1,2) in the leading term on the unit interval $(0,1)$. Generally the standard Galerkin finite element method can only give a low-order convergence even if the source term is very smooth due to the presence of the singularity term x^{\al-1} in the solution representation. In order to enhance the convergence, we develop a simple singularity reconstruction strategy by splitting the solution into a singular part and a regular part, where the former captures explicitly the singularity. We derive a new variational formulation for the regular part, and establish that the Galerkin approximation of the regular part can achieve a better convergence order in the $L^2(0,1)$, H^{\al/2}(0,1) and $L^\infty(0,1)$-norms than the standard Galerkin approach, with a convergence rate for the recovered singularity strength identical with the $L^2(0,1)$ error estimate. The reconstruction approach is very flexible in handling explicit singularity, and it is further extended to the case of a Neumann type boundary condition on the left end point, which involves a strong singularity x^{\al-2}. Extensive numerical results confirm the theoretical study and efficiency of the proposed approach.Comment: 23 pp. ESAIM: Math. Model. Numer. Anal., to appea

An Analysis of Galerkin Proper Orthogonal Decomposition for Subdiffusion

In this work, we develop a novel Galerkin-L1-POD scheme for the subdiffusion model with a Caputo fractional derivative of order $\alpha\in (0,1)$ in time, which is often used to describe anomalous diffusion processes in heterogeneous media. The nonlocality of the fractional derivative requires storing all the solutions from time zero. The proposed scheme is based on continuous piecewise linear finite elements, L1 time stepping, and proper orthogonal decomposition (POD). By constructing an effective reduced-order scheme using problem-adapted basis functions, it can significantly reduce the computational complexity and storage requirement. We shall provide a complete error analysis of the scheme under realistic regularity assumptions by means of a novel energy argument. Extensive numerical experiments are presented to verify the convergence analysis and the efficiency of the proposed scheme.Comment: 25 pp, 5 figure

Microstructural Characterization of Shrouded Plasma-Sprayed Titanium Coatings

Titanium and its alloys are often used for corrosion protection because they are able to offer high chemical resistance against various corrosive media. In this paper, shrouded plasma spray technology was applied to produce titanium coatings. A solid shroud with an external shrouding gas was used to plasma spray titanium powder feedstock with aim of reducing the oxide content in the as-sprayed coatings. The titanium coatings were assessed by optical microscope, scanning electron microscopy, X-ray diffraction, LECO combustion method and Vickers microhardness testing. The results showed that the presence of the shroud and the external shrouding gas led to a dense microstructure with a low porosity in the plasma-sprayed titanium coatings. The oxygen and nitrogen contents in the titanium coating were kept at a low level due to the shielding effect of the shroud attachment and the external shrouding gas. The dominant phase in the shrouded titanium coatings was mainly composed of α-Ti phase, which was very similar to the titanium feedstock powders. The shrouded plasma-sprayed titanium coatings had a Vickers microhardness of 404.2 ± 103.2 HV

Multi-View Active Learning in the Non-Realizable Case

The sample complexity of active learning under the realizability assumption has been well-studied. The realizability assumption, however, rarely holds in practice. In this paper, we theoretically characterize the sample complexity of active learning in the non-realizable case under multi-view setting. We prove that, with unbounded Tsybakov noise, the sample complexity of multi-view active learning can be $\widetilde{O}(\log\frac{1}{\epsilon})$, contrasting to single-view setting where the polynomial improvement is the best possible achievement. We also prove that in general multi-view setting the sample complexity of active learning with unbounded Tsybakov noise is $\widetilde{O}(\frac{1}{\epsilon})$, where the order of $1/\epsilon$ is independent of the parameter in Tsybakov noise, contrasting to previous polynomial bounds where the order of $1/\epsilon$ is related to the parameter in Tsybakov noise.Comment: 22 pages, 1 figur