Projects / Dynamical systems & time
Khresmoi: forecasting dynamical systems
Neural Koopman operators, HiPPO memories, synthetic gradients and symbolic dynamics, tested on synthetic oscillators and chaotic systems.
- Learning Koopman Invariant Subspaces (LKIS) and the Koopman Neural Forecaster
- HiPPO-LegS, including a transformer with a HiPPO encoder
- Synthetic gradients computed with
torch.func - A symbolic forecaster built on residual vector quantization
- Contrastive learning with matrix-power layers
- Test systems: FitzHugh–Nagumo, coupled FHN, and a smooth time-series generator