An adaptive adjoint-oriented neural network for solving parametric optimal control problems with singularities

Fuente: arXiv
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Autori principali: Yuan, Zikang, Wang, Guanjie, Liao, Qifeng
Natura: Preprint
Pubblicazione: 2025
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author Yuan, Zikang
Wang, Guanjie
Liao, Qifeng
author_facet Yuan, Zikang
Wang, Guanjie
Liao, Qifeng
contents In this work, we present an adaptive adjoint-oriented neural network (adaptive AONN) for solving parametric optimal control problems governed by partial differential equations. The proposed method integrates deep adaptive sampling techniques with the adjoint-oriented neural network (AONN) framework. It alleviates the limitations of AONN in handling low-regularity solutions and enhances the generalizability of deep adaptive sampling for surrogate modeling without labeled data ($\text{DAS}^2$). The effectiveness of the adaptive AONN is demonstrated through numerical examples involving singularities.
format Preprint
id arxiv_https___arxiv_org_abs_2512_18548
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle An adaptive adjoint-oriented neural network for solving parametric optimal control problems with singularities
Yuan, Zikang
Wang, Guanjie
Liao, Qifeng
Optimization and Control
49M41, 68T07, 62D05
In this work, we present an adaptive adjoint-oriented neural network (adaptive AONN) for solving parametric optimal control problems governed by partial differential equations. The proposed method integrates deep adaptive sampling techniques with the adjoint-oriented neural network (AONN) framework. It alleviates the limitations of AONN in handling low-regularity solutions and enhances the generalizability of deep adaptive sampling for surrogate modeling without labeled data ($\text{DAS}^2$). The effectiveness of the adaptive AONN is demonstrated through numerical examples involving singularities.
title An adaptive adjoint-oriented neural network for solving parametric optimal control problems with singularities
topic Optimization and Control
49M41, 68T07, 62D05
url https://arxiv.org/abs/2512.18548