Alternatives and similar repositories for SDE:

Users that are interested in SDE are comparing it to the libraries listed below

• AaltoML / BayesNewton
  Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.
  ☆232Updated last year
• jacobjinkelly / easy-neural-ode
  Code for the paper "Learning Differential Equations that are Easy to Solve"
  ☆278Updated 3 years ago
• xwinxu / bayeSDE
  Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"
  ☆169Updated 3 years ago
• openopt / copt
  A Python library for mathematical optimization
  ☆139Updated 5 months ago
• matt-graham / mici
  Manifold Markov chain Monte Carlo methods in Python
  ☆226Updated 2 months ago
• ColCarroll / minimc
  Just a little MCMC
  ☆223Updated 8 months ago
• secondmind-labs / GPflux
  Deep GPs built on top of TensorFlow/Keras and GPflow
  ☆124Updated 5 months ago
• hughsalimbeni / bayesian_benchmarks
  A community repository for benchmarking Bayesian methods
  ☆110Updated 3 years ago
• cagatayyildiz / npde
  Nonparametric Differential Equation Modeling
  ☆53Updated last year
• DiffEqML / diffeqml-research
  This repository contains code released by DiffEqML Research
  ☆89Updated 3 years ago
• JTT94 / filterflow
  Extensible Tensorflow library for differentiable particle filtering. ICML 2021.
  ☆41Updated 2 years ago
• AaltoML / kalman-jax
  Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX
  ☆98Updated last year
• otokonoko8 / deep-Bayesian-nonparametrics-papers
  The collection of papers about combining deep learning and Bayesian nonparametrics
  ☆120Updated 5 years ago
• izmailovpavel / understandingbdl
  ☆240Updated 2 years ago
• TeaPearce / Bayesian_NN_Ensembles
  AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'
  ☆89Updated 4 years ago
• oxcsml / ML_bazaar
  OxCSML research group reading groups and meetings at the Department of Statistics, University of Oxford.
  ☆93Updated 3 years ago
• bayesiains / nsf
  Code for Neural Spline Flows paper
  ☆266Updated 4 years ago
• Tom-Ryder / VIforSDEs
  A variational method for fast, approximate inference for stochastic differential equations.
  ☆44Updated 6 years ago
• cfinlay / ffjord-rnode
  Regularized Neural ODEs (RNODE)
  ☆82Updated 3 years ago
• IPAIopen / NumDL-CourseNotes
  Course notes for graduate-level class on numerical methods for deep learning
  ☆50Updated 4 years ago
• johannbrehmer / manifold-flow
  Manifold-learning flows (ℳ-flows)
  ☆229Updated 4 years ago
• GaussianProcessesCambridge / meetup-resources
  Collection of resources from each meetup event
  ☆13Updated 4 years ago
• eth-cscs / abcpy
  ABCpy package
  ☆113Updated 10 months ago
• brain-research / nngp
  Deep neural network kernel for Gaussian process
  ☆203Updated 4 years ago
• titu1994 / tfdiffeq
  Tensorflow implementation of Ordinary Differential Equation Solvers with full GPU support
  ☆218Updated 4 years ago
• mattja / sdeint
  Numerical integration of Ito or Stratonovich SDEs
  ☆165Updated last year
• mfinzi / constrained-hamiltonian-neural-networks
  ☆105Updated 3 years ago
• wesselb / stheno
  Gaussian process modelling in Python
  ☆222Updated 3 months ago
• j-wilson / GPflowSampling
  Code for efficiently sampling functions from GP(flow) posteriors
  ☆70Updated 4 years ago
• blei-lab / variational-smc
  Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)
  ☆65Updated 5 years ago