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We present a new deterministic approach for the solution of the Boltzmann kinetic equation based on nodal discontinuous Galerkin (DG) discretizations in velocity space. In the new approach the collision operator has the form of a bilinear…

Computational Physics · Physics 2018-01-19 Alexander Alekseenko , Eswar Josyula

A tensor decomposition approach for the solution of high-dimensional, fully nonlinear Hamilton-Jacobi-Bellman equations arising in optimal feedback control of nonlinear dynamics is presented. The method combines a tensor train approximation…

Optimization and Control · Mathematics 2021-03-17 Sergey Dolgov , Dante Kalise , Karl Kunisch

The Direct Simulation Monte Carlo (DSMC) method is widely employed for simulating rarefied nonequilibrium gas flows. With advances in aerospace engineering and micro/nano-scale technologies, gas flows exhibit the coexistence of rarefied and…

Computational Physics · Physics 2025-07-01 Hao Jin , Sha Liu , Sirui Yang , Junzhe Cao , Congshan Zhuo , Chengwen Zhong

In this paper we solve the Boltzmann transport equation using AI libraries. The reason why this is attractive is because it enables one to use the highly optimised software within AI libraries, enabling one to run on different computer…

Computational Engineering, Finance, and Science · Computer Science 2023-01-26 T. R. F. Phillips , C. E. Heaney , C. Boyang , A. G. Buchan , C. C. Pain

In this paper, we propose a general framework for solving high-dimensional partial differential equations with tensor networks. Our approach uses Monte-Carlo simulations to update the solution and re-estimates the new solution from samples…

Numerical Analysis · Mathematics 2025-12-12 Yian Chen , Yuehaw Khoo , Ziang Yu

Emerging tensor network techniques for solutions of Partial Differential Equations (PDEs), known for their ability to break the curse of dimensionality, deliver new mathematical methods for ultrafast numerical solutions of high-dimensional…

Numerical Analysis · Mathematics 2024-02-29 Dibyendu Adak , Duc P. Truong , Gianmarco Manzini , Kim Ø. Rasmussen , Boian S. Alexandrov

Dynamic mode decomposition (DMD) is a recently developed tool for the analysis of the behavior of complex dynamical systems. In this paper, we will propose an extension of DMD that exploits low-rank tensor decompositions of potentially…

Numerical Analysis · Mathematics 2019-08-14 Stefan Klus , Patrick Gelß , Sebastian Peitz , Christof Schütte

In this work we present recent results on application of low-rank tensor decompositions to modelling of aggregation kinetics taking into account multi-particle collisions (for three and more particles). Such kinetics can be described by…

Numerical Analysis · Mathematics 2020-08-18 Daniil A. Stefonishin , Sergey A. Matveev , Dmitry A. Zheltkov

We present new results building on the conservative deterministic spectral method for the space inhomogeneous Boltzmann equation developed by Gamba and Tharkabhushaman. This approach is a two-step process that acts on the weak form of the…

Numerical Analysis · Mathematics 2012-11-06 Irene M. Gamba , Jeffrey R. Haack

In this paper, we first extend the micro-macro decomposition method for multiscale kinetic equations from the BGK model to general collisional kinetic equations, including the Boltzmann and the Fokker-Planck Landau equations. The main idea…

Numerical Analysis · Mathematics 2019-02-20 Irene M. Gamba , Shi Jin , Liu Liu

In [C. Mouhot and L. Pareschi, "Fast algorithms for computing the Boltzmann collision operator," Math. Comp., to appear; C. Mouhot and L. Pareschi, C. R. Math. Acad. Sci. Paris, 339 (2004), pp. 71-76], fast deterministic algorithms based on…

Analysis of PDEs · Mathematics 2016-08-16 Francis Filbet , Clément Mouhot , Lorenzo Pareschi

Large tensors are frequently encountered in various fields such as computer vision, scientific simulations, sensor networks, and data mining. However, these tensors are often too large for convenient processing, transfer, or storage.…

Optimization and Control · Mathematics 2024-09-26 Zhiguang Cheng , Gaohang Yu , Xiaohao Cai , Liqun Qi

We propose an accurate, efficient, and low-memory sum-of-Gaussians tensor neural network (SOG-TNN) algorithm for solving the high-dimensional Schr\"{o}dinger equation. The SOG-TNN utilizes a low-rank tensor product representation of the…

Computational Physics · Physics 2026-03-05 Qi Zhou , Teng Wu , Jianghao Liu , Qingyuan Sun , Hehu Xie , Zhenli Xu

Numerically solving the Boltzmann kinetic equations with the small Knudsen number is challenging due to the stiff nonlinear collision term. A class of asymptotic preserving schemes was introduced in [6] to handle this kind of problems. The…

Numerical Analysis · Mathematics 2010-09-20 Francis Filbet , Jingwei Hu , Shi Jin

Spectral methods provide highly accurate numerical solutions for partial differential equations, exhibiting exponential convergence with the number of spectral nodes. Traditionally, in addressing time-dependent nonlinear problems, attention…

Numerical Analysis · Mathematics 2024-06-05 Dibyendu Adak , M. Engin Danis , Duc P. Truong , Kim Ø. Rasmussen , Boian S. Alexandrov

This research addresses the numerical simulation of the Boltzmann transport equation for semiconductor devices by proposing a multidimensional self-adaptive numerical simulation framework. This framework is applied to two important…

Numerical Analysis · Mathematics 2025-09-22 Zeyu Zhang , Xiaoyu Zhang , Zhigang Song , Qing Fang

Accurately evaluating configurational integrals for dense solids remains a central and difficult challenge in the statistical mechanics of condensed systems. Here, we present a novel tensor network approach that reformulates the…

Computational modeling and simulation of fluid-structure interactions constitute a fundamental cornerstone for advancing aerospace engineering endeavors. This paper addresses the notion and implementation of the immersed boundary method for…

Computational Physics · Physics 2025-12-24 Longqing Ge , Qingdong Cai , Yonghao Zhang , Tianbai Xiao

In this manuscript, we introduce the tensor-train reduced basis method, a novel projection-based reduced-order model designed for the efficient solution of parameterized partial differential equations. While reduced-order models are widely…

Numerical Analysis · Mathematics 2025-05-06 Nicholas Mueller , Yiran Zhao , Santiago Badia , Tiangang Cui

We introduce a family of numerical algorithms for the solution of linear system in higher dimensions with the matrix and right hand side given and the solution sought in the tensor train format. The proposed methods are rank--adaptive and…

Numerical Analysis · Mathematics 2014-10-07 Sergey V. Dolgov , Dmitry V. Savostyanov