Yongle Liu

Ling Guo, Yongle Liu, Tao Zhou

In this work, we combine the idea of data-driven polynomial chaos expansions with the weighted least-square approach to solve uncertainty quantification (UQ) problems. The idea of data-driven polynomial chaos is to use statistical moments of the input random variables to develop an arbitrary polynomial chaos expansion, and then use such data-driven bases to perform UQ computations. Here we adopt the bases construction procedure by following \cite{Ahlfeld_2016SAMBA}, where the bases are computed by using matrix operations on the Hankel matrix of moments. Different from previous works, in the postprocessing part, we propose a weighted least-squares approach to solve UQ problems. This approach includes a sampling strategy and a least-squares solver. The main features of our approach are two folds: On one hand, our sampling strategy is independent of the random input. More precisely, we propose to sampling with the equilibrium measure, and this measure is also independent of the data-driven bases. Thus, this procedure can be done in prior (or in a off-line manner). On the other hand, we propose to solve a Christoffel function weighted least-square problem, and this strategy is quasi-linearly stable -- the required number of PDE solvers depends linearly (up to a logarithmic factor) on the number of (data-driven) bases. This new approach is thus promising in dealing with a class of problems with epistemic uncertainties. Several numerical tests are presented to show the effectiveness of our approach.

Rémi Abgrall, Philipp Öffner, Yongle Liu

In this paper, we provide a few new properties of Active Flux (AF)/Point-Average-Moment PolynomiAl-interpreted (\pampa) schemes. First, we show, in full generality, that the AF/pampa schemes can be interpreted in such a way that the discontinuous Galerkin (dG) scheme is one of their building blocks. Secondly we provide intrinsic bound preserving properties of the current variant of pampa. This is also illustrated numerically. Last, we show, at least in one dimension, that the pampa scheme has the summation by part (SBP) property.

Ling Guo, Jing Li, Yongle Liu

We study the properties of sparse reconstruction of transformed $\ell_1$ (TL1) minimization and present improved theoretical results about the recoverability and the accuracy of this reconstruction from undersampled measurements. We then combine this method with the stochastic collocation approach to identify the coefficients of sparse orthogonal polynomial expansions for uncertainty quantification. In order to implement the TL1 minimization, we use the DCA-TL1 algorithm which was introduced by Zhang and Xin. In particular, when recover non-sparse functions, we adopt an adaptive DCA-TL1 method to guarantee the sparest solutions. Various numerical examples, including sparse polynomial functions recovery and non-sparse analytical functions recovery are presented to demonstrate the recoverability and efficiency of this novel method and its potential for problems of practical interests.

Rémi Abgrall, Yongle Liu

We propose an improved version of the PAMPA algorithm where the solution is sought as globally continuous. The scheme is locally conservative, and there is no mass matrix to invert. This method had been developed in a series of papers, see e.g \cite{Abgrall2024a} and the references therein. In \cite{Abgrall2025d}, we had shown the connection between PAMPA and the discontinuous Galerkin method, for the linear hyperbolic problem. Taking advantage of this reinterpretation, we use it to define a family of methods, show how to implement the boundary conditions in a rigorous manner. In addition, we propose a method that complements the bound preserving method developed in \cite{Abgrall2025d} in the sense that it is non oscillatory. A truncation error analysis is provided, it shows that the scheme should be third order accurate for smooth solutions. This is confirmed by numerical experiments. Several numerical examples are presented to show that the scheme is indeed bound preserving and non oscillatory on a wide range on numerical benchmarks.