Solar energy paper index
Physics-Informed Machine Learning for Bending Lifetime of Flexible Perovskite Dye-Sensitized Solar Cells (code & data)
One-line summary
A solar energy research paper on Physics-Informed Machine Learning for Bending Lifetime of Flexible Perovskite Dye-Sensitized Solar Cells (code & data).
Engineering notes
Engineering notes will be added by the Power for Solar editorial team.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。
Original abstract
Physics-informed machine-learning pipeline supporting the manuscript Physics Informed Machine Learning Prediction of Bending Lifetime in Flexible Perovskite Dye-Sensitized Solar Cells for Indoor Photovoltaics Using Multi-Physics Simulation Data. Contents: (i) a dataset-generation script implementing the classical-laminate, Coffin-Manson fatigue, and single-diode degradation models; (ii) the COMSOL finite-element model used to validate the laminate strain field (agreement within 0.06%); (iii) noise-robustness, sensitivity, and design-space analyses; and (iv) the cross-validated GPR/GBT/RF training pipeline (Python 3.12 + scikit learn, with an independent MATLAB R2024a implementation). Running gen_dataset_v3.py reproduces the 1807-sample low cycle fatigue dataset and all figures and tables reported in the manuscript.
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