Algorithm Developments for Discrete Adjoint Methods

By M. Giles, M. Duta, J. Mueller, Niles A. Pierce. 2003

364 citations recorded by the source corpus.

Subfields: Applied Mathematics, Computational Mechanics

View Paper: https://www.semanticscholar.org/paper/088bd47d67dc648325ff47aa23c2f8c3620690eb

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In Applied Mathematics

In Computational Mechanics

Citations in the corpus

Turbulence Modeling in the Age of Data (2018, methodology): “6) used sparse velocity eld data to infer the structure of the Reynolds stress magnitude and anisotropy. All these approaches involve large-scale statistical inference using adjoint-based algorithms (Giles et al. 2003) or derivative-free, iterative Ensemble Kalman method (Iglesias et al. 2013). As a”
Review of Output-Based Error Estimation and Mesh Adaptation in Computational Fluid Dynamics (2011, methodology): “Park solves the linear adjoint equation using a dual-consistent timemarching method [52,53] and adapts on the remaining error (Eq.”
Multidisciplinary design optimization: A survey of architectures (2013, methodology): “If the analysis codes require a particularly long time to evaluate, automatic differentiation or analytic derivative calculations (direct or adjoint methods) can be used to avoid multiple discipline analysis evaluations [93, 94, 95, 96, 97].”
Effective adjoint approaches for computational fluid dynamics (2019, methodology): “[25] developed an iterative primal-dual accurate approach to solve the adjoint equations.”
An implicit, exact dual adjoint solution method for turbulent flows on unstructured grids (2004, methodology): “The work is largely based on the recent contributions of Giles, [20–23] in”
Optimization and Computational Fluid Dynamics (2008, methodology): “He also presented the analytical solutions of the adjoint equations using Green’s functions [56], an exact approach for the solution of the adjoint equations [22], a shock wave reduction method in external aerodynamics [21] and the harmonic approach to turbomachinery steady and unsteady designs [11,”
Effect of Approximations of the Discrete Adjoint on Gradient-Based Optimization (2006, background): “where B is a projection matrix that extracts the velocity at the boundary, and then differentiating [16].”