AlexTMallen / koopman-forecasting
Long-term probabilistic forecasting of quasiperiodic phenomena using Koopman theory
☆34Updated 2 years ago
Related projects ⓘ
Alternatives and complementary repositories for koopman-forecasting
- Deep Probabilistic Koopman: long-term time-series forecasting under quasi-periodic uncertainty☆18Updated 3 years ago
- Dynamic mode decomposition with dependent structure among observables (Graph DMD)☆11Updated 4 years ago
- Implementation of a reservoir computer with functions to optimize the parameters and calculate the Lyapunov exponents☆26Updated 2 years ago
- ☆12Updated last year
- ☆40Updated 6 years ago
- ☆21Updated 4 years ago
- Consistent Koopman Autoencoders☆65Updated last year
- ☆27Updated 2 years ago
- ☆25Updated 6 years ago
- PySensors is a Python package for sparse sensor placement☆79Updated 3 months ago
- A Tensorflow based library for Time Series Modelling with Gaussian Processes☆28Updated 4 months ago
- State-space deep Gaussian processes in Python and Matlab☆29Updated 2 years ago
- AI4Science: Python/Matlab implementation of online and window dynamic mode decomposition (Online DMD and Window DMD)☆36Updated 2 years ago
- Linear and non-linear spectral forecasting algorithms☆132Updated 3 years ago
- Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…☆51Updated 2 years ago
- Deep learning assisted dynamic mode decomposition☆19Updated 3 years ago
- ☆30Updated 2 years ago
- Sparsity-promoting Kernel Dynamic Mode Decomposition for Nonlinear Dynamical Systems☆28Updated 2 years ago
- Code to accompany the paper "Discovery of Physics from Data: Universal Laws and Discrepancies"☆24Updated 4 years ago
- ☆19Updated 6 years ago
- Parametric Gaussian Process Regression for Big Data☆44Updated 4 years ago
- ☆19Updated last year
- A 30-minute showcase on the how and the why of neural differential equations.☆13Updated 7 months ago
- Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton☆24Updated 2 years ago
- Data-driven modeling of chaotic systems in the form of SDEs and nonlinear observation maps☆17Updated 4 years ago
- Uncertainty Quantification for Deep Spatiotemporal Forecasting☆21Updated 2 months ago
- ☆27Updated 10 months ago
- Time series forecasting with PyTorch☆82Updated this week
- a collection of modern sparse (regularized) linear regression algorithms.☆61Updated 4 years ago
- Implementation for Non-stationary Spectral Kernels (NIPS 2017)☆20Updated 4 years ago