17–22 May 2026
C.I.D
Europe/Zurich timezone

Real-Time X-ray Beamline Surrogate Modeling via a Physics-Informed Log-Manifold Learning Framework

TUP2715
19 May 2026, 16:00
2h
C.I.D

C.I.D

Deauville, France
Poster Presentation MC2.T26: Photon sources: Photon Beam Lines and Components Poster session

Speaker

Xuanying Song (Tsinghua University)

Description

Wave-optical simulation of undulator radiation through X-ray beamlines is computationally prohibitive, limiting real-time optimization. The high-frequency diffraction structures and extreme dynamic range of focal spot distributions pose significant challenges to conventional surrogate models. We propose a log-manifold surrogate modeling framework that represents intensity distributions in logarithmic space, converting highly nonlinear diffraction structures into low-rank learnable manifolds. With physics-informed OOD-aware Residual method, the model attains less than 1% relative error over the full dynamic range, faithfully reconstructs fine diffraction fringes, and generalizes robustly across beamline configurations. Single prediction takes only milliseconds, yielding thousands of speedup over SRW simulation and enabling real-time surrogate-based beamline optimization. This work demonstrates an efficient path toward real-time digital-twin beamline modeling for fourth-generation light sources, enabling online optimization, rapid parameter scans, and virtual diagnostics.

Footnotes

This work is supported by the Tsinghua SSMB program; the SSMB mechanism was demonstrated in Nature 590, 576 (2021).

Funding Agency

This work is supported by the National Natural Science Foundation of China (No.124B2109)

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Author

Xuanying Song (Tsinghua University)

Co-authors

Jiayi Sun (Tsinghua University) Lei Hu (Tsinghua University) Mr Haowei Hu (Tsinghua University) Chuanxiang Tang (Tsinghua University)

Presentation materials

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