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Efficient and stable solution of partial differential equations (PDEs) is central to scientific and engineering applications, yet existing numerical solvers rely heavily on matrix based discretizations, while learning based methods require…

Machine Learning · Computer Science 2026-04-30 Yi Bing , Zheng Ran , Fu Jinyang , Liu Long , Peng Xiang

The article addresses the convergence of implicit and semi-implicit, fully discrete approximations of a class of nonlinear parabolic evolution problems. Such schemes are popular in the numerical solution of evolutions defined with the…

Numerical Analysis · Mathematics 2019-02-22 Sören Bartels , Michael Růžička

The numerical approximation of partial differential equations (PDEs) poses formidable challenges in high dimensions since classical grid-based methods suffer from the so-called curse of dimensionality. Recent attempts rely on a combination…

Machine Learning · Computer Science 2023-07-31 Lorenz Richter , Leon Sallandt , Nikolas Nüsken

Optimization problems with constraints in the form of a partial differential equation arise frequently in the process of engineering design. The discretization of PDE-constrained optimization problems results in large-scale linear systems…

Numerical Analysis · Mathematics 2012-01-04 Jacek Gondzio , Pavel Zhlobich

While many phenomena in physics and engineering are formally high-dimensional, their long-time dynamics often live on a lower-dimensional manifold. The present work introduces an autoencoder framework that combines implicit regularization…

Machine Learning · Computer Science 2023-12-07 Kevin Zeng , Carlos E. Pérez De Jesús , Andrew J. Fox , Michael D. Graham

Dynamic trees are mixtures of tree structured belief networks. They solve some of the problems of fixed tree networks at the cost of making exact inference intractable. For this reason approximate methods such as sampling or mean field…

Machine Learning · Computer Science 2013-01-18 Amos J. Storkey

Computing reduced-order models using non-intrusive methods is particularly attractive for systems that are simulated using black-box solvers. However, obtaining accurate data-driven models can be challenging, especially if the underlying…

Mathematical Physics · Physics 2024-01-03 Alberto Padovan , Blaine Vollmer , Daniel J. Bodony

Probabilistic solvers provide a flexible and efficient framework for simulation, uncertainty quantification, and inference in dynamical systems. However, like standard solvers, they suffer performance penalties for certain stiff systems,…

Numerical Analysis · Mathematics 2023-12-20 Nathanael Bosch , Philipp Hennig , Filip Tronarp

Sparse coding consists in representing signals as sparse linear combinations of atoms selected from a dictionary. We consider an extension of this framework where the atoms are further assumed to be embedded in a tree. This is achieved…

Machine Learning · Statistics 2011-08-18 Rodolphe Jenatton , Julien Mairal , Guillaume Obozinski , Francis Bach

This paper investigates two related optimal input selection problems for fixed (non-switched) and switched structured systems. More precisely, we consider selecting the minimum cost of inputs from a prior set of inputs, and selecting the…

Systems and Control · Electrical Eng. & Systems 2022-10-20 Yuan Zhang , Yuanqing Xia , Shenyu Liu , Zhongqi Sun

A parametric approach is developed to the method of S-tree diagrams and its generalization for investigation of the hierarchical substructure of $N$-body nonlinearly interacting systems (e.g., clusters of galaxies). The introduction of a…

Astrophysics · Physics 2007-05-23 Karen M. Bekarian , Anahit A. Melkonian

Many statistical estimators for high-dimensional linear regression are M-estimators, formed through minimizing a data-dependent square loss function plus a regularizer. This work considers a new class of estimators implicitly defined…

Statistics Theory · Mathematics 2022-02-15 Peng Zhao , Yun Yang , Qiao-Chu He

A new family of methods involving complex coefficients for the numerical integration of differential equations is presented and analyzed. They are constructed as linear combinations of symmetric-conjugate compositions obtained from a basic…

Numerical Analysis · Mathematics 2021-10-14 Fernando Casas , Alejandro Escorihuela-Tomàs

Neural ordinary differential equations (NODEs) are an effective approach for data-driven modeling of dynamical systems arising from simulations and experiments. One of the major shortcomings of NODEs, especially when coupled with explicit…

Numerical Analysis · Mathematics 2025-12-30 Allen Alvarez Loya , Daniel A. Serino , J. W. Burby , Qi Tang

A class of evolution quasistatic systems which leads, after a suitable time discretization, to recursive nonlinear programs, is considered and optimal control or identification problems governed by such systems are investigated. The…

Optimization and Control · Mathematics 2014-11-19 L. Adam , J. V. Outrata , T. Roubicek

We consider adaptive finite element methods for second-order elliptic PDEs, where the arising discrete systems are not solved exactly. For contractive iterative solvers, we formulate an adaptive algorithm which monitors and steers the…

Numerical Analysis · Mathematics 2021-07-14 Gregor Gantner , Alexander Haberl , Dirk Praetorius , Stefan Schimanko

While several classes of integer linear optimization problems are known to be solvable in polynomial time, far fewer tractability results exist for integer nonlinear optimization. In this work, we narrow this gap by identifying a broad…

Optimization and Control · Mathematics 2026-02-09 Alberto Del Pia

We define decorated $\alpha$-stable trees which are informally obtained from an $\alpha$-stable tree by blowing up its branchpoints into random metric spaces. This generalizes the $\alpha$-stable looptrees of Curien and Kortchemski, where…

Probability · Mathematics 2022-05-09 Delphin Sénizergues , Sigurdur Örn Stefánsson , Benedikt Stufler

We construct several variational integrators--integrators based on a discrete variational principle--for systems with Lagrangians of the form L = L_A + epsilon L_B, with epsilon << 1, where L_A describes an integrable system. These…

Astrophysics · Physics 2009-01-25 Will M. Farr

We introduce a new class of computationally tractable scattering problems in unbounded domains, which we call decomposable problems. In these decomposable problems, the computational domain can be split into a finite collection of…

Numerical Analysis · Mathematics 2024-11-19 Tristan Goodwill , Charles L. Epstein