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We consider mechanics of composite materials in which thin inclusions are modeled by lower-dimensional manifolds. By successively applying the dimensional reduction to junctions and intersections within the material, a geometry of…

Numerical Analysis · Mathematics 2019-03-06 Wietse M. Boon , Jan M. Nordbotten

Spatially distributed problems are often approximately modelled in terms of partial differential equations (PDEs) for appropriate coarse-grained quantities (e.g. concentrations). The derivation of accurate such PDEs starting from finer…

Quantitative Methods · Quantitative Biology 2009-11-13 Liang Qiao , Radek Erban , C. T. Kelley , Ioannis G. Kevrekidis

We study a system of partial differential equations with integer and fractional derivatives arising in the study of forced oscillatory motion of a viscoelastic rod. We propose a new approach considering a quotient of relations appearing in…

Mathematical Physics · Physics 2015-08-11 Teodor M. Atanackovic , Stevan Pilipovic , Dusan Zorica

In multi-phase fluid flow, fluid-structure interaction, and other applications, partial differential equations (PDEs) often arise with discontinuous coefficients and singular sources (e.g., Dirac delta functions). These complexities arise…

Numerical Analysis · Mathematics 2019-07-24 Chung-Nan Tzou , Samuel Stechmann

Numerical approximations of partial differential equations (PDEs) are routinely employed to formulate the solution of physics, engineering, and mathematical problems involving functions of several variables, such as the propagation of heat…

Numerically simulating deformations in thin elastic sheets is a challenging problem in computational mechanics due to destabilizing compressive stresses that result in wrinkling. Determining the location, structure, and evolution of…

Materials Science · Physics 2014-11-26 Michael Taylor , Benny Davidovitch , Zhanlong Qiu , Katia Bertoldi

Wall-models are essential for enabling large-eddy simulations (LESs) of realistic problems at high Reynolds numbers. The present study is focused on approaches that directly model the wall shear stress, specifically on filling the gap…

Fluid Dynamics · Physics 2015-12-01 Stefan Hickel , Emile Touber , Julien Bodart , Johan Larsson

In this work we present a data-driven method for the discovery of parametric partial differential equations (PDEs), thus allowing one to disambiguate between the underlying evolution equations and their parametric dependencies. Group…

Numerical Analysis · Mathematics 2018-06-05 Samuel Rudy , Alessandro Alla , Steven L. Brunton , J. Nathan Kutz

This paper presents a theory for the behaviour of isotropic-hardening/softening elastoplastic materials that do not have a preferred reference configuration. In spite of important differences, many ingredients of classical plasticity are…

Mathematical Physics · Physics 2011-01-11 José Jorge Nader

A theory of flow stress, including the yield strength is proposed for the class of PC materials with equilibrium defect structure (EDS), which is established in the PC material after series of $N_0$ similar treatments of severe plastic…

Materials Science · Physics 2021-03-09 Alexander A. Reshetnyak

Many physical problems can be modelled by partial differential equations on unknown domains. Several examples can easily be found in the dynamics of free interfaces in fluid dynamics, solid mechanics or in fluid-solid interactions. To solve…

Numerical Analysis · Mathematics 2018-10-25 Javier Rivero-Rodriguez , Miguel Perez-Saborid , Benoit Scheid

In this paper we consider a class of unfitted finite element methods for discretization of partial differential equations on surfaces. In this class of methods known as the Trace Finite Element Method (TraceFEM), restrictions or traces of…

Numerical Analysis · Mathematics 2017-07-06 Maxim A. Olshanskii , Arnold Reusken

We present a methodology combining neural networks with physical principle constraints in the form of partial differential equations (PDEs). The approach allows to train neural networks while respecting the PDEs as a strong constraint in…

Numerical Analysis · Mathematics 2021-09-06 Sebastian K. Mitusch , Simon W. Funke , Miroslav Kuchta

This article deals with a viscoplastic material model of overstress type. The model is based on a multiplicative decomposition of the deformation gradient into elastic and inelastic part. An additional multiplicative decomposition of…

Numerical Analysis · Mathematics 2015-05-13 A. V. Shutov , R. Kreissig

We present a framework for fine-tuning flow-matching generative models to enforce physical constraints and solve inverse problems in scientific systems. Starting from a model trained on low-fidelity or observational data, we apply a…

Machine Learning · Computer Science 2026-01-28 Jan Tauberschmidt , Sophie Fellenz , Sebastian J. Vollmer , Andrew B. Duncan

Partial complementary energy densities are introduced through partial Legendre transforms from the strain energy density of linear elasticity. They have mixed components of the strain and stress tensors. Mixed variational principles based…

Classical Physics · Physics 2024-09-30 Jiashi Yang

In this work, we present an adjoint-based method for discovering the underlying governing partial differential equations (PDEs) given data. The idea is to consider a parameterized PDE in a general form and formulate a PDE-constrained…

Optimization and Control · Mathematics 2025-09-23 Mohsen Sadr , Tony Tohme , Kamal Youcef-Toumi

Nonlinear partial differential equations (PDEs) are used to model dynamical processes in a large number of scientific fields, ranging from finance to biology. In many applications standard local models are not sufficient to accurately…

Numerical Analysis · Mathematics 2022-05-10 Victor Boussange , Sebastian Becker , Arnulf Jentzen , Benno Kuckuck , Loïc Pellissier

This paper introduces an adaptive time splitting technique for the solution of stiff evolutionary PDEs that guarantees an effective error control of the simulation, independent of the fastest physical time scale for highly unsteady…

Numerical Analysis · Mathematics 2012-04-10 Stéphane Descombes , Max Duarte , Thierry Dumont , Violaine Louvet , Marc Massot

A state-of-the-art deep domain decomposition method (D3M) based on the variational principle is proposed for partial differential equations (PDEs). The solution of PDEs can be formulated as the solution of a constrained optimization…

Machine Learning · Computer Science 2020-04-03 Ke Li , Kejun Tang , Tianfan Wu , Qifeng Liao
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