SebastianCallh / neural-ode-weather-forecast
How to train a neural ODE for time series/weather forecasting
☆34Updated last year
Related projects: ⓘ
- ☆35Updated 2 years ago
- ☆27Updated last year
- Probabilistic solvers for differential equations in JAX. Adaptive ODE solvers with calibration, state-space model factorisations, and cus…☆30Updated last month
- A PyTorch library for all things nonlinear control and reinforcement learning.☆42Updated 2 years ago
- Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…☆50Updated 2 years ago
- Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing☆118Updated 3 weeks ago
- Official Implementation of "Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics" …☆27Updated 2 years ago
- ☆100Updated 3 years ago
- ☆23Updated 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 2 months ago
- Training materials for ModelingToolkit and JuliaSim☆39Updated last year
- Methods and experiments for assumed density SDE approximations☆11Updated 2 years ago
- ☆68Updated 4 years ago
- A 30-minute showcase on the how and the why of neural differential equations.☆12Updated 5 months ago
- IterGP: Computation-Aware Gaussian Process Inference (NeurIPS 2022)☆38Updated last year
- Port-Hamiltonian Approach to Neural Network Training☆22Updated 4 years ago
- Reservoir computing utilities for scientific machine learning (SciML)☆206Updated last month
- Software to train neural networks via Koopman operator theory (see Dogra and Redman "Optimizing Neural Networks via Koopman Operator Theo…☆20Updated last year
- Code for the Paper "Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers"☆21Updated 4 months ago
- Fast uncertainty quantification for scientific machine learning (SciML) and differential equations☆65Updated 2 weeks ago
- Long-term probabilistic forecasting of quasiperiodic phenomena using Koopman theory☆34Updated 2 years ago
- ☆19Updated 2 years ago
- Efficient, Accurate, and Streamlined Training of Physics-Informed Neural Networks☆54Updated last week
- Neural Stochastic PDEs: resolution-invariant modelling of continuous spatiotemporal dynamics☆44Updated last year
- Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.☆69Updated last week
- Stochastic Optimization under Uncertainty in Python.☆34Updated last month
- Hamiltonian neural network implementation for Henon Heiles dynamical system learning mix of order and chaos☆11Updated 9 months ago
- SymDer: Symbolic Derivative Approach to Discovering Sparse Interpretable Dynamics from Partial Observations☆17Updated 2 years ago
- Code and experiments for the NeurIPS 2023 paper Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints☆11Updated 5 months ago
- Turning SymPy expressions into JAX functions☆42Updated 3 years ago