wzhi / ODElearning_INN
[ICML 2022] Learning Efficient and Robust Ordinary Differential \\ Equations via Invertible Neural Networks
☆9Updated last year
Related projects ⓘ
Alternatives and complementary repositories for ODElearning_INN
- ☆27Updated 2 years ago
- Supplementary code for the paper "Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces"☆41Updated last year
- Nonparametric Differential Equation Modeling☆51Updated 8 months ago
- Supplementary code for the NeurIPS 2020 paper "Matern Gaussian processes on Riemannian manifolds".☆27Updated 3 years ago
- Code for "'Hey, that's not an ODE:' Faster ODE Adjoints via Seminorms" (ICML 2021)☆86Updated 2 years ago
- By introducing a differentiable contact model, DiffCoSim extends the applicability of Lagrangian/Hamiltonian-inspired neural networks to …☆31Updated last year
- Riemannian Convex Potential Maps☆68Updated last year
- Methods and experiments for assumed density SDE approximations☆11Updated 2 years ago
- code for "Neural Conservation Laws A Divergence-Free Perspective".☆35Updated last year
- ☆34Updated 3 years ago
- Koopman Kernels for Learning Dynamical Systems from Trajectory Data☆22Updated last year
- Second-Order Neural ODE Optimizer, NeurIPS 2021 spotlight☆52Updated 3 years ago
- ☆18Updated last year
- Repository for Deterministic Particle Flow Control framework☆10Updated 2 years ago
- Reference implementation of Finite Element Networks as proposed in "Learning the Dynamics of Physical Systems from Sparse Observations wi…☆69Updated 6 months ago
- Refining continuous-in-depth neural networks☆39Updated 3 years ago
- Learning unknown ODE models with Gaussian processes☆26Updated 6 years ago
- Computing gradients and Hessians of feed-forward networks with GPU acceleration☆19Updated 9 months ago
- ☆19Updated last month
- A Julia implementation of sparse Gaussian processes via path-wise doubly stochastic variational inference.☆33Updated 4 years ago
- Code for efficiently sampling functions from GP(flow) posteriors☆66Updated 4 years ago
- Modular Gaussian Processes☆15Updated 2 years ago
- Re-implementation of Hamiltonian Generative Networks paper☆33Updated 2 years ago
- A differentiation API for PyTorch☆30Updated 4 years ago
- Neural Fixed-Point Acceleration for Convex Optimization☆29Updated 2 years ago
- A library implementing the kernels for and experiments using extrinsic gauge equivariant vector field Gaussian Processes☆25Updated 3 years ago
- Practical tools for quantifying how well a sample approximates a target distribution☆27Updated 4 years ago
- We simulate a wind tunnel, place a rectangular occlusion in it, and then use gradient descent to turn the occlusion into a wing.☆25Updated 4 years ago
- Differentiable interface to FEniCS for JAX☆50Updated 3 years ago
- ☆38Updated last year