Spectral Learning For Crack And Corrosion Risk Prediction
Abstract
This paper proposes a novel geometry aware spectral optimization framework that overcomes the limitations of existing spectral and singular value decomposition (SVD)-based regularization techniques in order to reduce gradient instability and decay through a reflector-based orthogonal parameterization, ensuring temporally coherent spectral evolution during training. Singular value perturbations are applied within a consistent spectral frame, and optimization is carried out directly in the factorized domain, enabling structure preserving and error driven learning. The proposed framework is applied to long short-term memory (LSTM) architectures for automated crack and corrosion risk forecasting. Empirical evaluations show improved predictive accuracy in long horizon sequence modeling, demonstrating the advantages of geometry consistent spectral learning for complex spatiotemporal inference tasks.
Recommended Citation
I. Y. Sheikh and S. Jagannathan, "Spectral Learning For Crack And Corrosion Risk Prediction," 2026 IEEE International Conference on AI and Data Analytics Icad 2026, Institute of Electrical and Electronics Engineers, Jan 2026.
The definitive version is available at https://doi.org/10.1109/ICAD69378.2026.11608866
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Keywords and Phrases
Deep Learning; Infrastructural Health; LSTM; Spectral Learning; Time-to-Attention
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2026 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2026
Comments
Intelligent Systems Center, Grant 69A3552348339