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.
Koopman operators in robot learning
4 Pith papers cite this work, alongside 5 external citations. Polarity classification is still indexing.
verdicts
UNVERDICTED 4representative citing papers
KFTD applies Koopman-Fourier mapping for continuous spatiotemporal ocean forecasting with a residual network and DPP loss, claiming 5.6% average MSE reduction and 4x speedup over diffusion baselines.
A SA-KLQR controller with tactile feedback enables real-time regulation of angle, pressure, and coverage for a deformable swab tool in food-safety sampling.
Applies sparsity-promoting DMD to weather simulation observables to identify sparse transient Koopman modes representing convective structures.
citing papers explorer
-
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.
-
KFTD: Koopman-Fourier Time-Differentiable Network for Continuous Ocean Spatiotemporal Forecasting
KFTD applies Koopman-Fourier mapping for continuous spatiotemporal ocean forecasting with a residual network and DPP loss, claiming 5.6% average MSE reduction and 4x speedup over diffusion baselines.
-
Data-Driven Contact-Aware Control Method for Real-Time Deformable Tool Manipulation: A Case Study in the Environmental Swabbing
A SA-KLQR controller with tactile feedback enables real-time regulation of angle, pressure, and coverage for a deformable swab tool in food-safety sampling.
-
Extracting transient Koopman modes from short-term weather simulations with sparsity-promoting dynamic mode decomposition
Applies sparsity-promoting DMD to weather simulation observables to identify sparse transient Koopman modes representing convective structures.