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A machine learning technique is proposed for quantifying uncertainty in power system dynamics with spatiotemporally correlated stochastic forcing. We learn one-dimensional linear partial differential equations for the probability density…

Machine Learning · Computer Science 2023-12-19 Tyler E. Maltba , Vishwas Rao , Daniel Adrian Maldonado

Consider the Restricted Planar Circular Three Body Problem (RPC3BP), which models the motion of a massless particle (Asteroid) under the gravitational influence of two massive bodies (the primaries) moving on circular orbits. By considering…

Mathematical Physics · Physics 2025-12-24 Marcel Guardia , José Lamas , Tere M-Seara

This paper proposes a model predictive controller for discrete-time linear systems with additive, possibly unbounded, stochastic disturbances and subject to chance constraints. By computing a polytopic probabilistic positively invariant set…

Optimization and Control · Mathematics 2024-09-23 Kai Wang , Kiet Tuan Hoang , Sébastien Gros

Accurate platform localization is an integral component of most robotic systems. As these robotic systems become more ubiquitous, it is necessary to develop robust state estimation algorithms that are able to withstand novel and…

Robotics · Computer Science 2019-10-15 Ryan M. Watson , Jason N. Gross , Clark N. Taylor , Robert C. Leishman

A direct numerical simulation of the three-dimensional elektrokinetic instability near a charge selective surface (electric membrane, electrode, or system of micro-/nanochannels) is carried out and analyzed. A special finite-difference…

Fluid Dynamics · Physics 2014-08-06 E. A. Demekhin , N. V. Nikitin , V. S. Shelistov

This paper exposes a novel exploratory formalism, which end goal is the numerical simulation of the dynamics of a cloud of particles weakly or strongly coupled with a turbulent fluid. Giventhe large panel of expertise of the list of…

Analysis of PDEs · Mathematics 2019-10-21 Ludovic Goudenège , Adam Larat , Julie Llobell , Marc Massot , David Mercier , Olivier Thomine , Aymeric Vié

A method is presented to solve a stochastic, nonlinear optimal control problem representative of spacecraft trajectory design under uncertainty. The problem is reformulated as a chance constrained nonlinear program, or what is known as a…

Optimization and Control · Mathematics 2024-01-05 William Fife , Pradipto Ghosh , Kyle DeMars

This paper studies optimal control problems of unknown linear systems subject to stochastic disturbances of uncertain distribution. Uncertainty about the stochastic disturbances is usually described via ambiguity sets of probability…

Systems and Control · Electrical Eng. & Systems 2023-06-30 Guanru Pan , Timm Faulwasser

Wind gusts, being inherently stochastic, can significantly influence the safety and performance of aircraft. This study investigates a three-dimensional uncertainty quantification (UQ) problem to explore how uncertainties in gust and flight…

Computational Engineering, Finance, and Science · Computer Science 2025-01-08 Bingran Wang , Michael Warner , Aoran Tian , Luca Scotzniovsky , John T. Hwang

To better understand the orbital dynamics of exoplanets around close binary stars, i.e., circumbinary planets (CBPs), we applied techniques from dynamical systems theory to a physically motivated set of solutions in the Circular Restricted…

Earth and Planetary Astrophysics · Physics 2023-03-08 Andrew Langford , Lauren M. Weiss

This paper focuses on the attitude motion of spacecraft systems featuring asymmetric spacecraft platforms and unbalanced rotors. Through perturbation expansion, the spacecraft dynamic equation is simplified as a linear periodically…

Dynamical Systems · Mathematics 2025-08-07 Jingyuan Wu , Wenhao Li , Guanhua Feng

We discuss the relation between three recent approaches of describing the dynamics and the spatial distribution of particles suspended in turbulent flows: phase-space singularities in the inertial particle dynamics (caustics), real-space…

Fluid Dynamics · Physics 2015-06-05 K. Gustavsson , E. Meneguz , M. Reeks , B. Mehlig

This paper presents a nonlinear model predictive control strategy for stochastic systems with general (state and input dependent) disturbances subject to chance constraints. Our approach uses an online computed stochastic tube to ensure…

Systems and Control · Electrical Eng. & Systems 2022-07-19 Henning Schlüter , Frank Allgöwer

In molecular dynamics simulations in the NPT ensemble at constant pressure, the size and shape of the periodic simulation box fluctuate with time. For particle images far from the origin, the rescaling of the box by the barostat results in…

Computational Physics · Physics 2023-03-17 Jakob Tómas Bullerjahn , Sören von Bülow , Maziar Heidari , Jérôme Hénin , Gerhard Hummer

A computational approach is developed for the design of continuous low thrust transfers in the planar circular restricted three-body problem. The transfer design method of invariant manifolds is extended with the addition of continuous low…

Optimization and Control · Mathematics 2015-10-12 Shankar Kulumani , Taeyoung Lee

This paper introduces a framework for Chance-Constrained Optimization with Complex Variables, addressing complex linear programming for both individual and joint probabilistic constraints in the complex domain. We first analyze the 3CP…

Optimization and Control · Mathematics 2026-05-25 Raneem Madani , Abdel Lisser , Zeno Toffano

In this paper, we consider the problem of optimizing the worst-case behavior of a partially observed system. All uncontrolled disturbances are modeled as finite-valued uncertain variables. Using the theory of cost distributions, we present…

Optimization and Control · Mathematics 2023-02-21 Aditya Dave , Nishanth Venkatesh , Andreas A. Malikopoulos

In this paper, we investigate the attitude tracking problem of uncertain flexible spacecraft systems subject to external disturbances. In sharp contrast to existing results, the dynamics of flexible spacecraft systems and external…

Optimization and Control · Mathematics 2024-03-20 Zean Bao , Maobin Lu , Fang Deng , Jie Chen

Auto-differentiation is a powerful tool for computing derivatives of simulation results with respect to given parameters. In this letter, we have applied this tool to investigate the instability of a dynamics system that is governed by 21…

Accelerator Physics · Physics 2026-03-05 Ji Qiang

Data-based discovery of effective, coarse-grained (CG) models of high-dimensional dynamical systems presents a unique challenge in computational physics and particularly in the context of multiscale problems. The present paper offers a…

Computational Physics · Physics 2020-08-26 Sebastian Kaltenbach , Phaedon-Stelios Koutsourelakis