wzhi / ODElearning_INN
[ICML 2022] Learning Efficient and Robust Ordinary Differential \\ Equations via Invertible Neural Networks
☆10Updated last year
Alternatives and similar repositories for ODElearning_INN:
Users that are interested in ODElearning_INN are comparing it to the libraries listed below
- ☆29Updated 2 years ago
- Methods and experiments for assumed density SDE approximations☆11Updated 3 years ago
- Learning unknown ODE models with Gaussian processes☆26Updated 6 years ago
- ☆34Updated 3 years ago
- ☆21Updated 5 months ago
- Supplementary code for the paper "Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces"☆42Updated last year
- Nonparametric Differential Equation Modeling☆53Updated last year
- Differentiable interface to FEniCS for JAX☆53Updated 3 years ago
- code for "Neural Conservation Laws A Divergence-Free Perspective".☆37Updated 2 years ago
- By introducing a differentiable contact model, DiffCoSim extends the applicability of Lagrangian/Hamiltonian-inspired neural networks to …☆34Updated 2 years ago
- Reference implementation of Finite Element Networks as proposed in "Learning the Dynamics of Physical Systems from Sparse Observations wi…☆70Updated 10 months ago
- Riemannian Convex Potential Maps☆67Updated 2 years ago
- Supplementary code for the NeurIPS 2020 paper "Matern Gaussian processes on Riemannian manifolds".☆28Updated 2 months ago
- Code for "'Hey, that's not an ODE:' Faster ODE Adjoints via Seminorms" (ICML 2021)☆87Updated 2 years ago
- Computing gradients and Hessians of feed-forward networks with GPU acceleration☆18Updated last year
- ☆19Updated 2 years ago
- [NeurIPS'19] Deep Equilibrium Models Jax Implementation☆39Updated 4 years ago
- Code for efficiently sampling functions from GP(flow) posteriors☆72Updated 4 years ago
- Deterministic particle dynamics for simulating Fokker-Planck probability flows☆24Updated 2 years ago
- Turning SymPy expressions into JAX functions☆44Updated 4 years ago
- Repository for Deterministic Particle Flow Control framework☆10Updated 2 years ago
- Koopman Kernels for Learning Dynamical Systems from Trajectory Data☆24Updated last year
- Minimal Gaussian process library in JAX with a simple (custom) approach to state management.☆11Updated last year
- ☆30Updated 2 years ago
- Efficient Differentiable n-d PDE solvers in JAX.☆27Updated 4 months ago
- Practical tools for quantifying how well a sample approximates a target distribution☆27Updated 4 years ago
- Python trust-region subproblem solvers for nonlinear optimization☆29Updated 8 months ago
- ☆10Updated 3 years ago
- Port-Hamiltonian Approach to Neural Network Training☆22Updated 5 years ago
- AL4PDE: A Benchmark for Active Learning for Neural PDE Solvers☆21Updated last month