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Data-driven turbulence modeling is a newly emerged research area in thermal hydraulics simulation of nuclear power plant (NPP). The most common CFD method used in NPP thermal hydraulics simulation is Reynolds-averaged Navier-Stokes (RANS)…

Fluid Dynamics · Physics 2020-05-04 Yangmo Zhu , Nam Dinh

The classical continuous mixed formulation of linear elasticity with pointwise symmetric stresses allows for a conforming finite element discretization with piecewise polynomials of degree at least three. Symmetric stress approximations of…

Numerical Analysis · Mathematics 2025-03-17 Carsten Carstensen , Norbert Heuer

We extend the Data-Driven formulation of problems in elasticity of Kirchdoerfer and Ortiz (2016) to inelasticity. This extension differs fundamentally from Data-Driven problems in elasticity in that the material data set evolves in time as…

Computational Engineering, Finance, and Science · Computer Science 2019-04-24 Robert Eggersmann , Trenton Kirchdoerfer , Stefanie Reese , Laurent Stainier , Michael Ortiz

We study the global well-posedness and asymptotic behavior for a semilinear damped wave equation with Neumann boundary conditions, modelling a one-dimensional linearly elastic body interacting with a rigid substrate through an adhesive…

The superposition of data sets with internal parametric self-similarity is a longstanding and widespread technique for the analysis of many types of experimental data across the physical sciences. Typically, this superposition is performed…

Data Analysis, Statistics and Probability · Physics 2022-06-01 Kyle R. Lennon , Gareth H. McKinley , James W. Swan

Dislocation based modeling of plasticity is one of the central challenges at the crossover of materials science and continuum mechanics. Developing a continuum theory of dislocations requires the solution of two long standing problems: (i)…

Materials Science · Physics 2016-01-20 Thomas Hochrainer

The data-compatibility approach to constrained optimization, proposed here, strives to a point that is "close enough" to the solution set and whose target function value is "close enough" to the constrained minimum value. These notions can…

Optimization and Control · Mathematics 2020-10-26 Yair Censor , Maroun Zaknoon , Alexander J. Zaslavski

When discretizing symmetric stress tensors in variational problems arising in continuum mechanics, one has to choose how to enforce the symmetry of the stress tensor: (i) strongly by requiring the discrete tensors to be pointwise symmetric…

Numerical Analysis · Mathematics 2026-05-21 Pablo Brubeck , Charles Parker , Umberto Zerbinati

Homogeneous relaxation is a ubiquitous phenomenon in semiclassical kinetic theories where the quasiparticles are distributed uniformly in space, and the equilibration involves only their velocity distribution. For such solutions, the…

High Energy Physics - Theory · Physics 2015-03-19 Ramakrishnan Iyer , Ayan Mukhopadhyay

We study a technique for verification of stress and pressure computations on boundaries in flow simulations. We utilize existing experiments to provide validation of the simulations. We show that this approach can reveal critical flaws in…

Fluid Dynamics · Physics 2023-06-22 Ingeborg G. Gjerde , L. Ridgway Scott

We present a comparison between two approaches to modelling hyperelastic material behaviour using data. The first approach is a novel approach based on Data-driven Computational Mechanics (DDCM) that completely bypasses the definition of a…

Computational Engineering, Finance, and Science · Computer Science 2024-09-23 Martin Zlatić , Felipe Rocha , Laurent Stainier , Marko Čanađija

A variational coarse-graining framework for heterogeneous media is developed that allows for a seamless transition from the traditional static scenario to a arbitrary loading conditions, including inertia effects and body forces. The…

Materials Science · Physics 2015-10-09 Chenchen Liu , Celia Reina

Data-driven models analyze power grids under incomplete physical information, and their accuracy has been mostly validated empirically using certain training and testing datasets. This paper explores error bounds for data-driven models…

Machine Learning · Computer Science 2020-05-27 Yuxiao Liu , Bolun Xu , Audun Botterud , Ning Zhang , Chongqing Kang

When the underlying conditional density is known, conditional expectations can be computed analytically or numerically. When, however, such knowledge is not available and instead we are given a collection of training data, the goal of this…

Machine Learning · Statistics 2024-07-19 George V. Moustakides

Vertical loads acting on the surface of a half-space made of discrete and elastic particles are supported by a network of force chains that changes with the specific realization of the packing. These force chains can be transformed into…

Soft Condensed Matter · Physics 2019-09-20 Ignacio G. Tejada

An important mathematical tool in the analysis of dynamical systems is the approximation of the reach set, i.e., the set of states reachable after a given time from a given initial state. This set is difficult to compute for complex systems…

Machine Learning · Computer Science 2023-09-19 Abdelmouaiz Tebjou , Goran Frehse , Faïcel Chamroukhi

The Koopman operator framework can be used to identify a data-driven model of a nonlinear system. Unfortunately, when the data is corrupted by noise, the identified model can be biased. Additionally, depending on the choice of lifting…

Systems and Control · Electrical Eng. & Systems 2024-09-02 Louis Lortie , James Richard Forbes

We present a complete analytical solution for the stress field inside a homogeneous, inside a homogeneous, linearly elastic solid sphere subjected to a concentrated normal load applied on its surface. Starting from the three-dimensional…

Classical Physics · Physics 2026-05-06 Yosuke Mori , Kiwamu Yoshii , Satoshi Takada

The purpose of the current work is the development of an approach to account for quasi-static mechanical equilibrium in empirical (i.e., data-based) models for the stress field employing neural approximations (NAs), which include neural…

Computational Engineering, Finance, and Science · Computer Science 2026-05-04 Mohammad S. Khorrami , Pawan Goyal , Soroush Motahari , David Oexle , Jaber R. Mianroodi , Bob Svendsen , Peter Benner , Dierk Raabe

We revisit stress problems in linear elasticity to provide a perspective from the geometrical and functionalanalytic points of view. For the static stress problem of linear elasticity with mixed boundary conditions we write the associated…

Analysis of PDEs · Mathematics 2024-10-25 Ivan Gudoshnikov , Michal Křížek
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