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Journal ArticleDOI

Multiphysics simulations: Challenges and opportunities

David E. Keyes, +44 more
- Vol. 27, Iss: 1, pp 4-83
TLDR
This study considers multiphysics applications from algorithmic and architectural perspectives, where “algorithmic” includes both mathematical analysis and computational complexity, and “architectural’ includes both software and hardware environments.
Abstract
We consider multiphysics applications from algorithmic and architectural perspectives, where “algorithmic” includes both mathematical analysis and computational complexity, and “architectural” includes both software and hardware environments. Many diverse multiphysics applications can be reduced, en route to their computational simulation, to a common algebraic coupling paradigm. Mathematical analysis of multiphysics coupling in this form is not always practical for realistic applications, but model problems representative of applications discussed herein can provide insight. A variety of software frameworks for multiphysics applications have been constructed and refined within disciplinary communities and executed on leading-edge computer systems. We examine several of these, expose some commonalities among them, and attempt to extrapolate best practices to future systems. From our study, we summarize challenges and forecast opportunities.

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Citations
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CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences

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OpenMDAO: an open-source framework for multidisciplinary design, analysis, and optimization

TL;DR: The theory and architecture of OpenMDAO is presented, an open-source MDO framework that uses Newton-type algorithms to solve coupled systems and exploits problem structure through new hierarchical strategies to achieve high computational efficiency.
Journal ArticleDOI

MOOSE: Enabling Massively Parallel Multiphysics Simulation.

TL;DR: The Multiphysics Object Oriented Simulation Environment (MOOSE) aims to enable development by providing simplified interfaces for specification of partial differential equations, boundary conditions, material properties, and all aspects of a simulation without the need to consider the parallel, adaptive, nonlinear, finite-element solve that is handled internally.
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Numerical simulation of laminar reacting flows with complex chemistry

TL;DR: An adaptive algorithm for low Mach number reacting flows with complex chemistry that uses an operator-split treatment of stiff reaction terms and includes effects of differential diffusion is presented.
References
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Book

Neural Networks: A Comprehensive Foundation

Simon Haykin
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Journal ArticleDOI

GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems

TL;DR: An iterative method for solving linear systems, which has the property of minimizing at every step the norm of the residual vector over a Krylov subspace.
Book

Theory of Dislocations

TL;DR: Dislocations in Isotropic Continua: Effects of Crystal Structure on Dislocations and Dislocation-Point-Defect Interactions at Finite temperatures.
Journal ArticleDOI

Unified Approach for Molecular Dynamics and Density-Functional Theory

TL;DR: In this article, a unified scheme combining molecular dynamics and density-functional theory is presented, which makes possible the simulation of both covalently bonded and metallic systems and permits the application of density functional theory to much larger systems than previously feasible.
Journal ArticleDOI

Methods of Conjugate Gradients for Solving Linear Systems

TL;DR: An iterative algorithm is given for solving a system Ax=k of n linear equations in n unknowns and it is shown that this method is a special case of a very general method which also includes Gaussian elimination.
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