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Dynamic Mode Decomposition (DMD) is a data-driven technique to identify a low dimensional linear time invariant dynamics underlying high-dimensional data. For systems in which such underlying low-dimensional dynamics is time-varying, a…

Signal Processing · Electrical Eng. & Systems 2020-04-09 Mustaffa Alfatlawi , Vaibhav Srivastava

The characterization of intermittent, multiscale and transient dynamics using data-driven analysis remains an open challenge. We demonstrate an application of the Dynamic Mode Decomposition (DMD) with sparse sampling for the diagnostic…

Dynamical Systems · Mathematics 2020-05-18 Krithika Manohar , Eurika Kaiser , Steven L. Brunton , J. Nathan Kutz

We focus on Partial Differential Equation (PDE) based Data Assimilatio problems (DA) solved by means of variational approaches and Kalman filter algorithm. Recently, we presented a Domain Decomposition framework (we call it DD-DA, for…

Machine Learning · Computer Science 2022-04-01 Rosalba Cacciapuoti , Luisa D'Amore

Data assimilation (DA) methods use priors arising from differential equations to robustly interpolate and extrapolate data. Popular techniques such as ensemble methods that handle high-dimensional, nonlinear PDE priors focus mostly on state…

Machine Learning · Statistics 2024-06-05 Rafael Anderka , Marc Peter Deisenroth , So Takao

A multiscale numerical method is proposed for the solution of semi-linear elliptic stochastic partial differential equations with localized uncertainties and non-linearities, the uncertainties being modeled by a set of random parameters. It…

Numerical Analysis · Mathematics 2019-01-23 Anthony Nouy , Florent Pled

Combinatorial optimization problems are computationally hard in general, but they are ubiquitous in our modern life. A coherent Ising machine (CIM) based on a multiple-pulse degenerate optical parametric oscillator (DOPO) is an alternative…

In the domain of geometry and topology optimization, discovering geometries that optimally satisfy specific problem criteria is a complex challenge in both engineering and scientific research. In this work, we propose a new approach for the…

Computational Physics · Physics 2024-11-26 Alexander Luce , Daniel Grünbaum , Florian Marquardt

In this paper, we investigate Dual-Primal Isogeometric Tearing and Interconnecting (IETI-DP) methods for conforming Galerkin discretizations on multi-patch computational domains with inexact subdomain solvers. Recently, the authors have…

Numerical Analysis · Mathematics 2021-10-13 Rainer Schneckenleitner , Stefan Takacs

In this paper, we present the isogeometric least-squares collocation (IGA-L) method, which determines the numerical solution by making the approximate differential operator fit the real differential operator in a least-squares sense. The…

Numerical Analysis · Mathematics 2018-04-19 Hongwei Lin , Yunyang Xiong , Xiao Wang , Qianqian Hu

A surrogate model approximates the outputs of a solver of Partial Differential Equations (PDEs) with a low computational cost. In this article, we propose a method to build learning-based surrogates in the context of parameterized PDEs,…

Machine Learning · Computer Science 2024-06-28 Alejandro Ribés , Nawfal Benchekroun , Théo Delagnes

Quantum or quantum-inspired Ising machines have recently shown promise in solving combinatorial optimization problems in a short time. Real-world applications, such as time division multiple access (TDMA) scheduling for wireless multi-hop…

Emerging Technologies · Computer Science 2025-04-03 Yohei Hamakawa , Tomoya Kashimata , Masaya Yamasaki , Kosuke Tatsumura

Finding correspondences between shapes is a fundamental problem in computer vision and graphics, which is relevant for many applications, including 3D reconstruction, object tracking, and style transfer. The vast majority of correspondence…

Computer Vision and Pattern Recognition · Computer Science 2024-04-04 Maolin Gao , Zorah Lähner , Johan Thunberg , Daniel Cremers , Florian Bernard

We develop an automated computational modeling framework for rapid gradient-based design of multistable soft mechanical structures composed of non-identical bistable unit cells with appropriate geometric parameterization. This framework…

Numerical Analysis · Mathematics 2023-09-12 Mehran Mirramezani , Deniz Oktay , Ryan P. Adams

We propose and analyze a domain decomposition solver for the biharmonic problem. The problem is discretized in a conforming way using multi-patch Isogeometric Analysis. As first step, we discuss the setup of a sufficiently smooth…

Numerical Analysis · Mathematics 2025-11-10 Stefan Takacs

The numerical solution of large-scale PDEs, such as those occurring in data-driven applications, unavoidably require powerful parallel computers and tailored parallel algorithms to make the best possible use of them. In fact, considerations…

Numerical Analysis · Mathematics 2017-05-11 Francisco Bernal , Gonçalo dos Reis , Greig Smith

Elliptically-contoured distributions (ECD) play a significant role, in computer vision, image processing, radar, and biomedical signal processing. Maximum likelihood. estimation (MLE) of ECD leads to a system of non-linear equations,…

Signal Processing · Electrical Eng. & Systems 2020-11-06 Jialun Zhou , Salem Said , Yannick Berthoumieu

We present an approximately $C^1$-smooth multi-patch spline construction which can be used in isogeometric analysis (IGA). A key property of IGA is that it is simple to achieve high order smoothness within a single patch. To represent more…

Numerical Analysis · Mathematics 2022-10-12 Pascal Weinmüller , Thomas Takacs

In this paper we discuss the numerical solution on a simple 2D domain of the Helmoltz equation with mixed boundary conditions. The so called radiation problem depends on the wavenumber constant parameter k and it is inspired here by medical…

Exact diagonalization (ED) is a cornerstone technique in quantum many-body physics, enabling precise solutions to the Schr\"odinger equation for interacting quantum systems. Despite its utility in studying ground states, excited states, and…

Surrogate models for computational simulations are input-output approximations that allow computationally intensive analyses, such as uncertainty propagation and inference, to be performed efficiently. When a simulation output does not…

Computational Engineering, Finance, and Science · Computer Science 2014-08-05 Alex A. Gorodetsky , Youssef M. Marzouk