SebastianCallh / neural-ode-weather-forecast
How to train a neural ODE for time series/weather forecasting
☆39Updated last year
Alternatives and similar repositories for neural-ode-weather-forecast:
Users that are interested in neural-ode-weather-forecast are comparing it to the libraries listed below
- ☆28Updated 2 years ago
- Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…☆54Updated 2 years ago
- Methods and experiments for assumed density SDE approximations☆11Updated 2 years ago
- ☆30Updated last year
- ☆36Updated 3 years ago
- Neural Stochastic PDEs: resolution-invariant modelling of continuous spatiotemporal dynamics☆49Updated 2 years ago
- ☆48Updated last month
- Port-Hamiltonian Approach to Neural Network Training☆22Updated 5 years ago
- ☆71Updated 4 years ago
- Pytorch implementation of the DeepMoD algorithm: [arXiv:1904.09406]☆32Updated last year
- Multiwavelets-based operator model☆57Updated 2 years ago
- Solving high-dimensional Partial Differential Equations with Deep Learning☆24Updated 5 years ago
- Efficient Differentiable n-d PDE solvers in JAX.☆22Updated 2 months ago
- A 30-minute showcase on the how and the why of neural differential equations.☆13Updated 9 months ago
- Linear and non-linear spectral forecasting algorithms☆135Updated 3 years ago
- Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton☆25Updated 2 years ago
- Probabilistic solvers for differential equations in JAX. Adaptive ODE solvers with calibration, state-space model factorisations, and cus…☆38Updated 2 months ago
- SINDy (Sparse Identification of Nonlinear Dynamics) algorithms☆71Updated 2 years ago
- Solving High Dimensional Partial Differential Equations with Deep Neural Networks☆33Updated 3 years ago
- ☆20Updated 2 months ago
- Neural parameter calibration for multi-agent models. Uses neural networks to estimate marginal densities on parameters and networks☆28Updated last month
- Accompanying code for "Weak form generalized Hamiltonian learning"☆9Updated 3 years ago
- Software to train neural networks via Koopman operator theory (see Dogra and Redman "Optimizing Neural Networks via Koopman Operator Theo…☆19Updated last year
- Sparse Identification of Nonlinear Dynamics for Hybrid Systems☆22Updated 6 years ago
- The code enables to perform Bayesian inference in an efficient manner through the use of Hamiltonian Neural Networks (HNNs), Deep Neural …☆13Updated 2 years ago
- Consistent Koopman Autoencoders☆67Updated last year
- Sparsity-promoting Kernel Dynamic Mode Decomposition for Nonlinear Dynamical Systems☆28Updated 2 years ago
- ☆30Updated 2 years ago
- Benchmark for learning stiff problems using physics-informed machine learning☆11Updated 3 years ago
- Probabilistic ODE solvers are fun, but are they fast? See also: https://github.com/pnkraemer/probdiffeq for JAX code or https://github.c…☆20Updated 5 months ago