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We develop a novel iterative direct sampling method (IDSM) for solving linear or nonlinear elliptic inverse problems with partial Cauchy data. It integrates three innovations: a data completion scheme to reconstruct missing boundary…

Numerical Analysis · Mathematics 2025-11-12 Bangti Jin , Fengru Wang , Jun Zou

Several algorithms are presented for the accurate computation of the leaves in the foliation of an ODE near a hyperbolic fixed point. They are variations of a contraction mapping method in [25] to compute inertial manifolds, which…

Numerical Analysis · Mathematics 2012-11-06 Y. -M. Chung , M. S. Jolly

We present a computational framework for estimating the uncertainty in the numerical solution of linearized infinite-dimensional statistical inverse problems. We adopt the Bayesian inference formulation: given observational data and their…

Numerical Analysis · Mathematics 2013-08-07 Tan Bui-Thanh , Omar Ghattas , James Martin , Georg Stadler

Finite dimensional solutions to a class of stochastic partial differential equations are obtained extending the differential constraints method for deterministic PDE to the stochastic framework. A geometrical reformulation of the stochastic…

Probability · Mathematics 2017-12-25 Francesco C. De Vecchi

In this article, we provide a general strategy based on Lyapunov functionals to analyse global asymptotic stability of linear infinite-dimensional systems subject to nonlinear dampings under the assumption that the origin of the system is…

Analysis of PDEs · Mathematics 2018-08-17 Swann Marx , Yacine Chitour , Christophe Prieur

The simulation of large nonlinear dynamical systems, including systems generated by discretization of hyperbolic partial differential equations, can be computationally demanding. Such systems are important in both fluid and kinetic…

Plasma Physics · Physics 2021-06-14 Alexander Engel , Graeme Smith , Scott E. Parker

Learning stable dynamical systems from data is crucial for safe and reliable robot motion planning and control. However, extending stability guarantees to trajectories defined on Riemannian manifolds poses significant challenges due to the…

In recent years, stochastic effects have become increasingly relevant for describing fluid behaviour, particularly in the context of turbulence. The most important model for inviscid fluids in computational fluid dynamics are the Euler…

Numerical Analysis · Mathematics 2024-12-11 Dominic Breit , Thamsanqa Castern Moyo , Philipp Öffner

In this work we study the dynamic behaviour of compound shells of revolution partially filled with an ideal incompressible fluid based on boundary-value problems. New analytical mathematical model with corresponding discrete scheme for the…

Computational Engineering, Finance, and Science · Computer Science 2016-06-15 Iryna Kononenko , Oleksiy Kononenko

We extend classical finite-dimensional Fenichel theory in two directions to infinite dimensions. Under comparably weak assumptions we show that the solution of an infinite-dimensional fast-slow system is approximated well by the…

Dynamical Systems · Mathematics 2020-08-26 Felix Hummel , Christian Kuehn

Intrinsic nonlinear elasticity deals with the deformations of elastic bodies as isometric immersions of Riemannian manifolds into the Euclidean spaces (see Ciarlet [9,10]). In this paper, we study the rigidity and continuity properties of…

Analysis of PDEs · Mathematics 2026-02-24 Gui-Qiang G. Chen , Siran Li , Marshall Slemrod

We develop a theoretical framework for the analysis of stabilized cut finite element methods for the Laplace-Beltrami operator on a manifold embedded in $\mathbb{R}^d$ of arbitrary codimension. The method is based on using continuous…

Numerical Analysis · Mathematics 2016-10-07 Erik Burman , Peter Hansbo , Mats G. Larson , Andre Massing

In this paper we consider a class of linear time invariant systems with infinitely many unstable modes. By using the parameterization of all stabilizing controllers, we show that H-infinity controllers for such systems can be computed using…

Systems and Control · Electrical Eng. & Systems 2020-03-03 Suat Gumussoy , Hitay Ozbay

We discuss the efficient implementation of a high-performance second-order collocation-type finite-element scheme for solving the compressible Euler equations of gas dynamics on unstructured meshes. The solver is based on the convex…

Mathematical Software · Computer Science 2021-02-04 Matthias Maier , Martin Kronbichler

We present a novel approach that integrates unfitted finite element methods and neural networks to approximate partial differential equations on complex geometries. Easy-to-generate background meshes (e.g., a simple Cartesian mesh) that cut…

Numerical Analysis · Mathematics 2025-12-04 Wei Li , Alberto F. Martín , Santiago Badia

The aim of this paper is to prove the existence and smoothness of stable and unstable invariant manifolds for a stochastic delayed partial differential equation of parabolic type. The stochastic delayed partial differential equation is…

Dynamical Systems · Mathematics 2023-06-13 Wenjie Hu , Quanxin Zhu , Tomás Caraballo

A new method is proposed to numerically integrate a dynamical system on a manifold such that the trajectory stably remains on the manifold and preserves first integrals of the system. The idea is that given an initial point in the manifold…

Numerical Analysis · Mathematics 2016-11-29 Dong Eui Chang , Fernando Jimenez , Matthew Perlmutter

A Skeleton-stabilized ImmersoGeometric Analysis technique is proposed for incompressible viscous flow problems with moderate Reynolds number. The proposed formulation fits within the framework of the finite cell method, where essential…

We consider the inverse problem of determining the metric-measure structure of collapsing manifolds from local measurements of spectral data. In the part I of the paper, we proved the uniqueness of the inverse problem and a continuity…

Analysis of PDEs · Mathematics 2024-04-26 Matti Lassas , Jinpeng Lu , Takao Yamaguchi

Nonlinear dimensionality reduction methods provide a valuable means to visualize and interpret high-dimensional data. However, many popular methods can fail dramatically, even on simple two-dimensional manifolds, due to problems such as…

Machine Learning · Statistics 2020-07-08 Daniel Ting , Michael I. Jordan