Research map / Probabilistic and scientific modeling / Dynamical systems
Spectral Learning of Dynamic Systems from Nonequilibrium Data
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Enforce equilibrium constraint (solve for equilibrium initial vector omega_eq via quadratic program) on spectral-learned OOM | discrete OOM spectral learning from nonequilibrium data · Brownian dynamics two-well process | statistical bias in Prob_I / Prob_II estimates | Helped |
| Use binless spectral learning instead of binned/density-based estimators for continuous data | continuous OOM learning on nonequilibrium data · Brownian dynamics stationary distribution | accuracy of stationary distribution estimate | Helped |
| Use binless spectral learning for continuous data | continuous OOM learning on 2D diffusion · 2D diffusion TICA eigenvector estimation | estimation error of w_TICA | Helped |
| Use binless spectral learning instead of other estimators | molecular dynamics of alanine dipeptide · alanine dipeptide stationary distribution pi | estimation error of pi | Helped |
Closest papers by meaning
- Universal Learning of Nonlinear Dynamics (ICML 2026 · similarity 0.87)
- Learning invariant representations of time-homogeneous stochastic dynamical systems (ICLR 2024 · similarity 0.86)
- Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups (ICML 2025 · similarity 0.85)
- Consistent Long-Term Forecasting of Ergodic Dynamical Systems (ICML 2024 · similarity 0.84)
- ResKoopNet: Learning Koopman Representations for Complex Dynamics with Spectral Residuals (ICML 2025 · similarity 0.84)
- From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach (NeurIPS 2024 · similarity 0.84)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.