Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
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Updated
Jun 29, 2024 - Python
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
For doing multidimensional recurrent quantification analysis(MdRQA) and sliding window version of it
A package for the sparse identification of nonlinear dynamical systems from data
Inclusive model of expression dynamics with conventional or metabolic labeling based scRNA-seq / multiomics, vector field reconstruction and differential geometry analyses
Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
Python package which takes advantage of Numba to efficiently implement a variety of coherent structure methods and analyze time-dependent dynamical systems.
Python implementation of the redundancy algorithm to estimate KS entropy
Galactic and Gravitational Dynamics in Python (+ GPU and autodiff)
Open-source, graph-based Python code generator and analysis toolbox for dynamical systems (pre-implemented and custom models). Most pre-implemented models belong to the family of neural population models.
Package for the data-driven representation of non-linear dynamics over manifolds based on a statistical distribution of local phase portrait features. Includes specific example on dynamical systems, synthetic- and real neural datasets. https://agosztolai.github.io/MARBLE/
Python package for solving partial differential equations using finite differences.
Operator Inference for data-driven, non-intrusive model reduction of dynamical systems.
Dynamical System Identification using python incorporating numerous powerful deep learning methods. (deepSI = deep System Identification)
Tools for exploiting Morphological Symmetries in robotics
Generate DAEs of k-pendulums.
Tensor Train Toolbox
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
Simulation and visualization of dynamic systems.
Compute Lyapunov exponents and Covariant-Lyapunov-Vectors of an RNN update trajectory
Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.
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