Multiple-time-step latent space advancement, called kFNO and kCNN, reduces short-term prediction error and improves long-term statistics for chaotic flame front PDEs compared to standard FNO and CNN baselines.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
math.DS 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Koopman Theory-Inspired Method for Learning Time Advancement Operators in Unstable Flame Front Evolution
Multiple-time-step latent space advancement, called kFNO and kCNN, reduces short-term prediction error and improves long-term statistics for chaotic flame front PDEs compared to standard FNO and CNN baselines.