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We continue our investigation of finite deformation linear viscoelastodynamics by focusing on constructing accurate and reliable numerical schemes. The concrete thermomechanical foundation developed in the previous study paves the way for…

Numerical Analysis · Mathematics 2023-05-26 Ju Liu , Jiashen Guan

A new field of numerical astrophysics is introduced which addresses the solution of large, multidimensional structural or slowly-evolving problems (rotating stars, interacting binaries, thick advective accretion disks, four dimensional…

Astrophysics · Physics 2009-10-30 David L. Meier

To account for phenomenological theories and a set of invariants, stress and strain are usually decomposed into a pair of pressure and deviatoric stress and a pair of volumetric strain and deviatoric strain. However, the conventional…

Mathematical Physics · Physics 2012-11-15 HyunSuk Lee , Jinkyu Kim

A novel general purpose Finite Element framework is presented to study small-scale metal plasticity. A distinct feature of the adopted distortion gradient plasticity formulation, with respect to strain gradient plasticity theories, is the…

Materials Science · Physics 2017-11-16 Emilio Martínez-Pañeda , Christian F. Niordson , Lorenzo Bardella

This paper presents the first time implementation of the eXtended Finite Element Method (XFEM) in the general purpose commercial software COMSOL Multiphysics. An enrichment strategy is proposed, consistent with the structure of the…

Computational Engineering, Finance, and Science · Computer Science 2021-09-08 Ahmad Jafari , Pooyan Broumand , Mohammad Vahab , Nasser Khalili

This paper is concerned with mixed finite element method (FEM) for solving the two-dimensional, nonlinear fourth-order active fluid equations. By introducing an auxiliary variable $w=-\Delta u$, the original fourth problem is transformed…

Numerical Analysis · Mathematics 2025-07-30 Nan Zheng , Xu Guo , Wenlong Pei , Wenju Zhao

The statistical finite element method (StatFEM) is an emerging probabilistic method that allows observations of a physical system to be synthesised with the numerical solution of a PDE intended to describe it in a coherent statistical…

Numerical Analysis · Mathematics 2022-02-21 Yanni Papandreou , Jon Cockayne , Mark Girolami , Andrew B. Duncan

Many scientific phenomena are modeled by Partial Differential Equations (PDEs). The development of data gathering tools along with the advances in machine learning (ML) techniques have raised opportunities for data-driven identification of…

Machine Learning · Computer Science 2021-05-05 Kazem Meidani , Amir Barati Farimani

We propose a complement to constitutive modeling that augments neural networks with material principles to capture anisotropy and inelasticity at finite strains. The key element is a dual potential that governs dissipation, consistently…

Computational Engineering, Finance, and Science · Computer Science 2025-12-09 Hagen Holthusen , Ellen Kuhl

In fractured natural formations, the equations governing fluid flow and geomechanics are strongly coupled. Hydrodynamical properties depend on the mechanical configuration, and they are therefore difficult to accurately resolve using…

Numerical Analysis · Mathematics 2021-04-07 Matteo Cusini , Joshua A. White , Nicola Castelletto , Randolph R. Settgast

In this paper we present a thermodynamically consistent material model which is capable of modelling ductile-to brittle failure mode transition in ductile material undergoing deformations at high strain rates, and demonstrate the…

Materials Science · Physics 2017-09-19 Ladislav Écsi , Péter Ván , Tamás Fülöp , Balázs Fekete , Pavel Élesztős , Roland Jančo

The phase field fracture method has emerged as a promising computational tool for modelling a variety of problems including, since recently, hydrogen embrittlement and stress corrosion cracking. In this work, we demonstrate the potential of…

Applied Physics · Physics 2020-11-17 P. K. Kristensen , C. F. Niordson , E. Martínez-Pañeda

There is an ever-growing need for predictive models for the elasto-viscoplastic deformation of solids. Our goal in this paper is to incorporate recently developed out-of-equilibrium statistical concepts into a thermodynamically consistent,…

Materials Science · Physics 2015-06-18 Ken Kamrin , Eran Bouchbinder

The dynamic mode decomposition (DMD) is a data-driven method used for identifying the dynamics of complex nonlinear systems. It extracts important characteristics of the underlying dynamics using measured time-domain data produced either by…

Numerical Analysis · Mathematics 2020-11-24 Ion Victor Gosea , Igor Pontes Duff

This paper discusses the application of Dynamic Mode Decomposition (DMD) to the extraction of modal properties of linear mechanical systems, i.e., experimental modal analysis (EMA). First, theoretical background of the DMD is briefly…

Dynamical Systems · Mathematics 2026-03-17 Akira Saito , Tomohiro Kuno

We prove the convergence of an adaptive mixed finite element method (AMFEM) for (nonsymmetric) convection-diffusion-reaction equations. The convergence result holds from the cases where convection or reaction is not present to convection-or…

Numerical Analysis · Mathematics 2015-03-26 Shaohong Du , Xiaoping Xie

We consider within a finite element approach the usage of different adaptively refined meshes for different variables in systems of nonlinear, time-depended PDEs. To resolve different solution behaviours of these variables, the meshes can…

Numerical Analysis · Mathematics 2010-05-27 Thomas Witkowski , Axel Voigt

Achieving accurate numerical results of hydrodynamic loads based on the potential-flow theory is very challenging for structures with sharp edges, due to the singular behavior of the local-flow velocities. In this paper, we introduce the…

Numerical Analysis · Mathematics 2021-09-23 Ying Wang , Yanlin Shao , Jikang Chen , Hui Liang

Traditional sheet metal forming relies on time-consuming and expensive Finite Element Analysis (FEA) for design validation, a process that significantly prolongs design cycles. While surrogate models offer faster iteration, current…

Machine Learning · Computer Science 2026-05-20 Jiajie Luo , Mohamed Mohamed , Osama Hassan , Haosu Zhou , Yingxue Zhao , Haoran Li , Xinrun Li , Zhutao Shao , Yang Long , Nan Li , Jichun Li

We present a data-driven approach to characterizing nonidentifiability of a model's parameters and illustrate it through dynamic as well as steady kinetic models. By employing Diffusion Maps and their extensions, we discover the minimal…

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