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In dynamical systems, it is advantageous to identify regions of flow which can exhibit maximal influence on nearby behaviour. Hyperbolic Lagrangian Coherent Structures have been introduced to obtain two-dimensional surfaces which maximise…

Fluid Dynamics · Physics 2022-07-25 Jack Tyler , Alexander Wittig

In this paper, we propose a moment method to numerically solve the Vlasov equations using the framework of the NRxx method developed in [6, 8, 7] for the Boltzmann equation. Due to the same convection term of the Boltzmann equation and the…

Mathematical Physics · Physics 2012-09-05 Zhenning Cai , Ruo Li , Yanli Wang

Stochastic collocation methods for approximating the solution of partial differential equations with random input data (e.g., coefficients and forcing terms) suffer from the curse of dimensionality whereby increases in the stochastic…

Numerical Analysis · Mathematics 2014-05-23 Aretha L. Teckentrup , Peter Jantsch , Clayton G. Webster , Max Gunzburger

Over the last few years there have been dramatic advances in our understanding of mathematical and computational models of complex systems in the presence of uncertainty. This has led to a growth in the area of uncertainty quantification as…

Numerical Analysis · Mathematics 2013-06-05 Maziar Raissi , Padmanabhan Seshaiyer

In this paper, we propose a conservative low rank tensor method to approximate nonlinear Vlasov solutions. The low rank approach is based on our earlier work (arxiv: 2106.08834). It takes advantage of the fact that the differential…

Numerical Analysis · Mathematics 2022-01-26 Wei Guo , Jing-Mei Qiu

Stochastic Gradient Langevin Dynamics infuses isotropic gradient noise to SGD to help navigate pathological curvature in the loss landscape for deep networks. Isotropic nature of the noise leads to poor scaling, and adaptive methods based…

Machine Learning · Computer Science 2019-06-13 Chandrasekaran Anirudh Bhardwaj

This paper formulates a variational approach for treating observational uncertainty and/or computational model errors as stochastic transport in dynamical systems governed by action principles under nonholonomic constraints. For this…

Classical Physics · Physics 2018-10-23 Darryl D Holm , Vakhtang Putkaradze

In this paper, we propose a dynamical low-rank (DLR) approximation framework for solving the semiclassical Schrodinger equation with uncertainties. The primary numerical challenges arise from the dual nature of the oscillations: the spatial…

Numerical Analysis · Mathematics 2026-02-23 Liu Liu , Limin Xu , Zhenyi Zhu

In this paper, we propose a high-order energy-conserving semi-Lagrangian discontinuous Galerkin(ECSLDG) method for the Vlasov-Ampere system. The method employs a semi-Lagrangian discontinuous Galerkin scheme for spatial discretization of…

Numerical Analysis · Mathematics 2025-04-30 Xiaofeng Cai , Qingtao Li , Hongtao Liu , Haibiao Zheng

Non-spherical particles transported by an anisotropic turbulent flow preferentially align with the mean shear and intermittently tumble when the local strain fluctuates. Such an intricate behaviour is here studied for inertialess,…

Soft Condensed Matter · Physics 2022-12-27 Lorenzo Campana , Mireille Bossy , Jeremie Bec

We propose an efficient semi-Lagrangian characteristic mapping method for solving the one+one-dimensional Vlasov-Poisson equations with high precision on a coarse grid. The flow map is evolved numerically and exponential resolution in…

Numerical Analysis · Mathematics 2024-05-14 Philipp Krah , Xi-Yuan Yin , Julius Bergmann , Jean-Christophe Nave , Kai Schneider

Motivated by the modeling of the spatial structure of the velocity field of three-dimensional turbulent flows, and the phenomenology of cascade phenomena, a linear dynamics has been recently proposed able to generate high velocity gradients…

In this paper, we develop a class of high order conservative semi-Lagrangian (SL) discontinuous Galerkin (DG) methods for solving multi-dimensional linear transport equations. The methods rely on a characteristic Galerkin weak formulation,…

Numerical Analysis · Mathematics 2017-09-25 Xiaofeng Cai , Wei Guo , Jing-Mei Qiu

In this work, we apply a semi-Lagrangian spectral method for the Vlasov-Poisson system, previously designed for periodic Fourier discretizations, by implementing Legendre polynomials and Hermite functions in the approximation of the…

Numerical Analysis · Mathematics 2018-07-09 Lorella Fatone , Daniele Funaro , Gianmarco Manzini

In this paper, we present a stochastic augmented Lagrangian approach on (possibly infinite-dimensional) Riemannian manifolds to solve stochastic optimization problems with a finite number of deterministic constraints.We investigate the…

Optimization and Control · Mathematics 2025-04-01 Caroline Geiersbach , Tim Suchan , Kathrin Welker

In this paper, we investigate stochastic partial differential equations driven by multi-parameter anisotropic fractional Levy noises, including the stochastic Poisson equation, the linear heat equation, and the quasi-linear heat equation.…

Probability · Mathematics 2014-10-07 Xuebin Lu , Wanyang Dai

High-resolution simulations of particle-based kinetic plasma models typically require a high number of particles and thus often become computationally intractable. This is exacerbated in multi-query simulations, where the problem depends on…

Numerical Analysis · Mathematics 2023-07-10 Jan S. Hesthaven , Cecilia Pagliantini , Nicolò Ripamonti

This article considers the variational wave equation with viscosity and transport noise as a system of three coupled nonlinear stochastic partial differential equations. We prove pathwise global existence, uniqueness, and temporal…

Analysis of PDEs · Mathematics 2026-01-08 Peter H. C. Pang

In this paper, we propose a modification of an acoustic-transport operator splitting Lagrange-projection method for simulating compressible flows with gravity. The original method involves two steps that respectively account for acoustic…

Numerical Analysis · Mathematics 2023-10-30 Rémi Bourgeois , Pascal Tremblin , Samuel Kokh , Thomas Padioleau

Stochastic optimization is a vital field in the realm of mathematical optimization, finding applications in diverse areas ranging from operations research to machine learning. In this paper, we introduce a novel first-order optimization…

Optimization and Control · Mathematics 2024-09-17 Vladimir Solodkin , Savelii Chezhegov , Ruslan Nazikov , Aleksandr Beznosikov , Alexander Gasnikov
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