Optimizing training data via a differentiable SCM yields climate emulators that outperform those trained on six standard ScenarioMIP pathways while using less data and isolating distinct forcing responses.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Koopman autoencoders with attention-free latent memory and online change-point re-encoding reduce long-horizon error on Duffing, Repressilator, and IRMA benchmarks while keeping low latency.
citing papers explorer
-
Optimal scenario design for climate emulation
Optimizing training data via a differentiable SCM yields climate emulators that outperform those trained on six standard ScenarioMIP pathways while using less data and isolating distinct forcing responses.
-
Learning the Koopman Operator using Attention Free Transformers
Koopman autoencoders with attention-free latent memory and online change-point re-encoding reduce long-horizon error on Duffing, Repressilator, and IRMA benchmarks while keeping low latency.