TRIE benchmarks stochastic PDE surrogates on two chaotic SPDEs, finding generative models best match long-term statistics and uncertainty while latent versions cut inference time by 12x.
Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach.Physical review letters, 120(2):024102
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Ensemble reservoir computing's prediction uncertainty serves as a data-driven indicator of local dynamical properties in spatiotemporal chaotic systems, matching known measures like Lyapunov spectra.
citing papers explorer
-
TRIE: An Evaluation Framework for Stochastic PDE Surrogates
TRIE benchmarks stochastic PDE surrogates on two chaotic SPDEs, finding generative models best match long-term statistics and uncertainty while latent versions cut inference time by 12x.
-
Data-driven characterization of spatiotemporal chaos using ensemble reservoir computing
Ensemble reservoir computing's prediction uncertainty serves as a data-driven indicator of local dynamical properties in spatiotemporal chaotic systems, matching known measures like Lyapunov spectra.