white-alistair / Stabilized-Neural-Differential-EquationsLinks
Code and experiments for the NeurIPS 2023 paper Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints
☆12Updated last year
Alternatives and similar repositories for Stabilized-Neural-Differential-Equations
Users that are interested in Stabilized-Neural-Differential-Equations are comparing it to the libraries listed below
Sorting:
- A Review of Sensitivity Methods for Differential Equations☆33Updated last year
- A minimal JAX library for connectivity modelling at scale☆11Updated 3 weeks ago
- ☆23Updated last month
- Physics-Enhanced Regression for Initial Value Problems☆20Updated last year
- Probabilistic ODE solvers are fun, but are they fast? See also: https://github.com/pnkraemer/probdiffeq for JAX code or https://github.c…☆21Updated last year
- A comprehensive collection of 35+ recurrent neural network layers for Flux.jl☆28Updated 2 months ago
- DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia☆31Updated last week
- ☆31Updated 3 years ago
- Lectures, code and material for the Modelling and Machine Learning of Dynamical Systems in Julia lecture at Technical University Munich☆15Updated 5 months ago
- A Julia package for constrained trajectory optimization using direct methods.☆29Updated 3 years ago
- Differentiable matrix factorizations using ImplicitDifferentiation.jl.☆31Updated 2 years ago
- Inverse modelling framework for dynamical systems characterised by complex dynamics.☆13Updated 3 months ago
- StateSpaceLearning.jl is a Julia package for time-series analysis using state space learning framework.☆20Updated 4 months ago
- ☆12Updated 4 years ago
- Checkpointing for Automatic Differentiation☆60Updated this week
- ☆18Updated 2 years ago
- Relational piecewise-linear overapproximations of multi-dimensional functions☆21Updated 3 months ago
- Weight Initialization Schemes for Deep Learning Frameworks☆10Updated last year
- Taylor-mode automatic differentiation for higher-order derivatives☆81Updated this week
- Learning to optimize (L2O) package that provides basic functionalities to help fit proxy models for optimization.☆15Updated 9 months ago
- Implements Optimization and approximate uncertainty quantification algorithms, Ensemble Kalman Inversion, and Ensemble Kalman Processes.☆114Updated this week
- Julia implementation of the Kolmogorov-Arnold network with custom gradients for fast training.☆82Updated last month
- Efficient, Accurate, and Streamlined Training of Physics-Informed Neural Networks☆59Updated 9 months ago
- A Julia package for robust neural networks.☆57Updated 5 months ago
- No need to train, he's a smooth operator☆45Updated last month
- MeshGraphNets.jl is a software package for the Julia programming language that provides an implementation of the MeshGraphNets framework …☆27Updated last month
- Implicit Layer Machine Learning via Deep Equilibrium Networks, O(1) backpropagation with accelerated convergence.☆59Updated this week
- Package Information and Documentation☆16Updated 3 years ago
- Julia implementation of Locally Feasibly Projected Sequential Quadratic Programming☆26Updated 4 years ago
- ☆48Updated 3 months ago