Research map / Learning theory and algorithms / Matrix and tensor methods
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use LKIS-DMD instead of OC-SVM for detecting sudden amplitude decays | Far-infrared laser time series · Far-infrared laser time series | AUC: 0.924 vs 0.799 | Helped |
| Use LKIS-DMD instead of RuLSIF for detecting sudden amplitude decays | Far-infrared laser time series · Far-infrared laser time series | AUC: 0.924 vs 0.803 | Helped |
| Learn observables by minimizing the residual sum of squares of linear least-squares regression | LKIS-DMD · Nonlinear dynamical systems | Koopman spectral analysis | Helped |
Closest papers by meaning
- ResKoopNet: Learning Koopman Representations for Complex Dynamics with Spectral Residuals (ICML 2025 · similarity 0.93)
- Efficient Parametric SVD of Koopman Operator for Stochastic Dynamical Systems (NeurIPS 2025 · similarity 0.92)
- Deep-Koopman-KANDy: Dictionary Discovery for Deep-Koopman Operators with Kolmogorov-Arnold Networks for Dynamics (NeurIPS 2026 · similarity 0.89)
- Finding Koopman Invariant Subspaces via Personalized PageRank (NeurIPS 2026 · similarity 0.88)
- KoopSTD: Reliable Similarity Analysis between Dynamical Systems via Approximating Koopman Spectrum with Timescale Decoupling (ICML 2025 · similarity 0.86)
- KoNODE: Koopman-Driven Neural Ordinary Differential Equations with Evolving Parameters for Time Series Analysis (ICML 2025 · similarity 0.85)
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