From analytical to data-driven: multi-method optimization of a bendable mirror system for dynamic focusing

Sun, B.; Wen, Z.; Zhu, Q.; Luo, J.; Xu, T.; Zhu, L.; Liang, Y.; Sun, J.; Li, Z.; Xia, S.; Xu, Z.; Wu, M.; Li, Q.; Zhang, W.; Yang, X.

doi:10.1107/S1600577526009422

Figure 11
Two-stage architecture of the TLNN surrogate model. In Stage 1, the base model predicts simulated surface profiles from end-force inputs through a multilayer network with a residual linear branch. In Stage 2, a trainable scaling vector and a residual correction network are introduced to refine the base-model prediction using measured data.

JOURNAL OF
SYNCHROTRON
RADIATION

ISSN: 1600-5775

Volume 33| Part 6| November 2026|

https://doi.org/10.1107/S1600577526009422

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