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We introduce the concept of data-driven finite element methods. These are finite-element discretizations of partial differential equations (PDEs) that resolve quantities of interest with striking accuracy, regardless of the underlying mesh…

Numerical Analysis · Mathematics 2022-11-15 Ignacio Brevis , Ignacio Muga , Kristoffer G. van der Zee

A class of exact penalty-type local search methods for optimal control problems with nonsmooth cost functional, nonsmooth (but continuous) dynamics, and nonsmooth state and control constraints is presented, in which the the penalty…

Optimization and Control · Mathematics 2023-02-21 M. V. Dolgopolik

Finite element methods have been successfully used to develop physics-based models of soft robots that capture the nonlinear dynamic behavior induced by continuous deformation. These high-fidelity models are therefore ideal for designing…

Robotics · Computer Science 2021-03-29 Sander Tonkens , Joseph Lorenzetti , Marco Pavone

We study the robust output regulation of linear boundary control systems by constructing extended systems. The extended systems are established based on solving static differential equations under two new conditions. We first consider the…

Optimization and Control · Mathematics 2021-04-19 Duy Phan , Lassi Paunonen

The Finite Element Method (FEM) is the gold standard for spatial discretization in numerical simulations for a wide spectrum of real-world engineering problems. Prototypical areas of interest include linear heat transfer and linear…

Numerical Analysis · Mathematics 2022-01-10 Marcelo Forets , Daniel Freire Caporale , Jorge M. Pérez Zerpa

In this paper, an abstract framework for the error analysis of discontinuous finite element method is developed for the distributed and Neumann boundary control problems governed by the stationary Stokes equation with control constraints.…

Numerical Analysis · Mathematics 2021-11-01 Asha K Dond , Thirupathi Gudi , Ramesh Ch. Sau

Turbulent dynamical systems are characterized by nonlinear interactions and stochastic effects that generate coupled statistical quantities, such as non-zero higher-order moments, which are difficult to capture from data with accuracy. We…

Machine Learning · Computer Science 2026-05-12 Xingjian Xu , Di Qi , Chunmei Wang

We study the problem of minimizing a strongly convex, smooth function when we have noisy estimates of its gradient. We propose a novel multistage accelerated algorithm that is universally optimal in the sense that it achieves the optimal…

Optimization and Control · Mathematics 2019-10-29 Necdet Serhat Aybat , Alireza Fallah , Mert Gurbuzbalaban , Asuman Ozdaglar

The finite element simulation of dynamic wetting phenomena, requiring the computation of flow in a domain confined by intersecting a liquid-fluid free surface and a liquid-solid interface, with the three-phase contact line moving across the…

Computational Physics · Physics 2012-02-20 J. E. Sprittles , Y. D. Shikhmurzaev

The maximum principle for optimal control problems of fully coupled forward-backward doubly stochastic differential equations (FBDSDEs in short) in the global form is obtained, under the assumptions that the diffusion coefficients do not…

Optimization and Control · Mathematics 2012-05-28 Liangquan Zhang , Yufeng Shi

We develop a novel gradient-based algorithm for optimizing nonsmooth nonconvex functions where nonsmoothness arises from explicit nonsmooth operators in the objective's analytical form. Our key innovation involves encoding active smooth…

Optimization and Control · Mathematics 2025-05-08 Fengqiao Luo

In this paper we study stochastic optimal control problems of fully coupled forward-backward stochastic differential equations (FBSDEs). The recursive cost functionals are defined by controlled fully coupled FBSDEs. We study two cases of…

Optimization and Control · Mathematics 2013-02-06 Juan Li , Qingmeng Wei

In this work, we propose an adaptive spectral element algorithm for solving nonlinear optimal control problems. The method employs orthogonal collocation at the shifted Gegenbauer-Gauss points combined with very accurate and stable…

Optimization and Control · Mathematics 2023-03-06 Kareem T. Elgindy

Recently, there has been great interest in connections between continuous-time dynamical systems and optimization methods, notably in the context of accelerated methods for smooth and unconstrained problems. In this paper we extend this…

Optimization and Control · Mathematics 2023-01-25 Guilherme França , Daniel P. Robinson , René Vidal

Solutions to optimal control problems can be discontinuous, even if all the functionals defining the problem are smooth. This can cause difficulties when numerically computing solutions to these problems. While conventional numerical…

Optimization and Control · Mathematics 2022-11-21 Lucian Nita , Eric C. Kerrigan , Eduardo M. G. Vila , Yuanbo Nie

In this paper, we consider the nonsmooth convex optimization problems over the fixed point constraint sets of firmly nonexpansive operators. To find an optimal solution of the problem, we present an iterative method based on the hybrid…

Optimization and Control · Mathematics 2026-03-23 Ontima Pankoon , Nimit Nimana , Yeol Je Cho

Using a recently introduced representation of the second order adjoint state as the solution of a function-valued backward stochastic partial differential equation (SPDE), we calculate the viscosity super- and subdifferential of the value…

Probability · Mathematics 2024-06-27 Wilhelm Stannat , Lukas Wessels

A numerical method is proposed for a class of stochastic control problems including singular behavior. This method solves an infinite-dimensional linear program equivalent to the stochastic control problem using a finite element type…

Probability · Mathematics 2018-06-11 Martin G. Vieten , Richard H. Stockbridge

The stability problem of a class of nonlinear switched systems defined on compact sets with state-dependent switching is considered. Instead of the Caratheodory solutions, the general Filippov solutions are studied. This encapsulates…

Optimization and Control · Mathematics 2017-07-31 Mohamadreza Ahmadi , Hamed Mojallali , Rafael Wisniewski

Dynamic Mode Decomposition (DMD) has emerged as a powerful tool for analyzing the dynamics of non-linear systems from experimental datasets. Recently, several attempts have extended DMD to the context of low-rank approximations. This…

Machine Learning · Statistics 2018-05-18 Patrick Héas , Cédric Herzet