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Award ID contains: 1845076

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  1. ; ; ; (, Transactions on machine learning research)
    Free, publicly-accessible full text available August 1, 2026
  2. ; ; (, IEEE Transactions on Signal Processing)
    Free, publicly-accessible full text available January 1, 2026
  3. ; (, IEEE Signal Processing Letters)
    Free, publicly-accessible full text available January 1, 2026
  4. ; (, Proceedings of the Third Learning on Graphs Conference (LoG 2024), PMLR 269)
    Free, publicly-accessible full text available November 16, 2025
  5. ; ; ; ; (, Proceedings of the 27th International Conference on Artifi- cial Intelligence and Statistics (AISTATS) 2024,)
    Accepted Manuscript Full Text Available
  6. ; ; ; ; (, Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS) 2024,)
    Accepted Manuscript Full Text Available
  7. ; ; ; (, Proceedings of the 41 st International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  8. ; ; ; ; (, Proceedings of the 41 st International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  9. ; ; ; (, Proceedings of the 41 st International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  10. ; ; ; (, Chaos: An Interdisciplinary Journal of Nonlinear Science)
    We present a novel method for learning reduced-order models of dynamical systems using nonlinear manifolds. First, we learn the manifold by identifying nonlinear structure in the data through a general representation learning problem. The proposed approach is driven by embeddings of low-order polynomial form. A projection onto the nonlinear manifold reveals the algebraic structure of the reduced-space system that governs the problem of interest. The matrix operators of the reduced-order model are then inferred from the data using operator inference. Numerical experiments on a number of nonlinear problems demonstrate the generalizability of the methodology and the increase in accuracy that can be obtained over reduced-order modeling methods that employ a linear subspace approximation. 
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