jacobjinkelly / easy-neural-ode
Code for the paper "Learning Differential Equations that are Easy to Solve"
☆269Updated 2 years ago
Related projects ⓘ
Alternatives and complementary repositories for easy-neural-ode
- Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"☆170Updated 2 years ago
- Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.☆420Updated last year
- Turning SymPy expressions into PyTorch modules.☆141Updated last year
- PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks☆422Updated 2 months ago
- Manifold-learning flows (ℳ-flows)☆230Updated 3 years ago
- A LinearOperator implementation to wrap the numerical nuts and bolts of GPyTorch☆93Updated 2 months ago
- Tensorflow implementation of Ordinary Differential Equation Solvers with full GPU support☆218Updated 4 years ago
- This repository contains code released by DiffEqML Research☆85Updated 2 years ago
- Gaussian processes in JAX.☆459Updated last week
- Deep GPs built on top of TensorFlow/Keras and GPflow☆120Updated 3 weeks ago
- This library would form a permanent home for reusable components for deep probabilistic programming. The library would form and harness a…☆301Updated last week
- Pytorch implementation of Augmented Neural ODEs☆530Updated last year
- Large-scale, multi-GPU capable, kernel solver☆181Updated 3 months ago
- Code for our paper "Hamiltonian Neural Networks"☆421Updated 3 years ago
- Probabilistic Numerics in Python.☆438Updated 6 months ago
- A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods☆1,395Updated 6 months ago
- ☆406Updated last month
- [NeurIPS 2020] Neural Manifold Ordinary Differential Equations (https://arxiv.org/abs/2006.10254)☆115Updated last year
- Example codes for the book Applied Stochastic Differential Equations☆182Updated 3 years ago
- Code for "'Hey, that's not an ODE:' Faster ODE Adjoints via Seminorms" (ICML 2021)☆86Updated 2 years ago
- Newton and Quasi-Newton optimization with PyTorch☆319Updated 7 months ago
- Jupyter notebook with Pytorch implementation of Neural Ordinary Differential Equations☆687Updated 8 months ago
- Reference implementation of Finite Element Networks as proposed in "Learning the Dynamics of Physical Systems from Sparse Observations wi…☆69Updated 5 months ago
- ODE2VAE: Deep generative second order ODEs with Bayesian neural networks☆123Updated 2 months ago
- Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)☆626Updated 2 years ago
- Numerical integration in arbitrary dimensions on the GPU using PyTorch / TF / JAX☆188Updated 4 months ago
- A parallel ODE solver for PyTorch☆225Updated last month
- Normalizing Flows using JAX☆82Updated 11 months ago
- [IJCAI'19, NeurIPS'19] Anode: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs☆104Updated 4 years ago
- Gaussian process modelling in Python☆218Updated 9 months ago