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- Title
A graph-based methodology for constructing computational models that automates adjoint-based sensitivity analysis.
- Authors
Gandarillas, Victor; Joshy, Anugrah Jo; Sperry, Mark Z.; Ivanov, Alexander K.; Hwang, John T.
- Abstract
The adjoint method provides an efficient way to compute sensitivities for system models with a large number of inputs. However, implementing the adjoint method requires significant effort that limits its use. The effort is exacerbated in large-scale multidisciplinary design optimization. We propose the adoption of a three-stage compiler as the method for constructing computational models for large-scale multidisciplinary design optimization to enable accurate and efficient adjoint sensitivity analysis. We develop a new modeling language called the Computational System Design Language that provides an appropriate input to the compiler front end that works well with multidisciplinary models. This paper describes the three-stage compiler methodology and the Computational System Design Language. The proposed solution uses a graph representation of the numerical model to automatically generate a computational model that computes adjoint-based sensitivities for use within an optimization framework. For two engineering models, this approach reduces the amount of user code by a factor of approximately two compared to their original implementations, without a measurable increase in computation time. This paper also includes a best-case complexity analysis that is built into the compiler implementation to allow users to estimate the memory required to evaluate a computational model and its derivatives, which is independent of the compiler back end that ultimately generates the computational model. Future compiler implementations are expected to approach the theoretical best-case memory cost and improve run time performance for both model evaluation and derivative computation.
- Subjects
ADJOINT differential equations; MULTIDISCIPLINARY design optimization; SENSITIVITY analysis; ENGINEERING models; SYSTEMS design; REPRESENTATIONS of graphs; AUTOMATIC differentiation
- Publication
Structural & Multidisciplinary Optimization, 2024, Vol 67, Issue 5, p1
- ISSN
1615-147X
- Publication type
Article
- DOI
10.1007/s00158-024-03792-0