Alternatives and similar repositories for companion:

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

• PredictiveIntelligenceLab / JAX-BO
  ☆30Updated 2 years ago
• PredictiveIntelligenceLab / GP-NODEs
  ☆29Updated 2 years ago
• udellgroup / orie7391
  Materials and syllabus for Cornell ORIE 7391, Faster: Algorithmic Ideas for Speeding Up Optimization
  ☆23Updated 2 years ago
• dynamicslab / langevin-regression
  Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton
  ☆25Updated 3 years ago
• SebastianCallh / neural-ode-weather-forecast
  How to train a neural ODE for time series/weather forecasting
  ☆38Updated 2 years ago
• lindonroberts / trust-region
  Python trust-region subproblem solvers for nonlinear optimization
  ☆29Updated 8 months ago
• gerdm / bayes
  Neat Bayesian machine learning examples
  ☆55Updated 2 months ago
• juliusberner / Deep-Multilevel-Kolmogorov-PDE-Solver
  Solving stochastic differential equations and Kolmogorov equations by means of deep learning and Multilevel Monte Carlo simulation
  ☆12Updated 3 years ago
• kpchamp / MultiscaleDiscovery
  ☆27Updated 6 years ago
• cagatayyildiz / npde
  Nonparametric Differential Equation Modeling
  ☆53Updated last year
• gianlucadetommaso / Stein-variational-samplers
  Python and MATLAB code for Stein Variational sampling methods
  ☆25Updated 5 years ago
• OxfordML / bayesquad
  Library for Bayesian Quadrature
  ☆31Updated 5 years ago
• stephenbeckr / convex-optimization-class
  APPM 5630 at CU Boulder
  ☆47Updated last week
• RajDandekar / MSML21_BayesianNODE
  ☆36Updated 3 years ago
• gatsby-sahani / gp-sde
  ☆14Updated last year
• Tom-Ryder / VIforSDEs
  A variational method for fast, approximate inference for stochastic differential equations.
  ☆44Updated 6 years ago
• juliusberner / oberwolfach_workshop
  Material for 'Mathematics of Deep Learning Workshop' (Invited Talk)
  ☆17Updated 2 years ago
• numericalalgorithmsgroup / pybobyqa
  Python-based Derivative-Free Optimization with Bound Constraints
  ☆78Updated 6 months ago
• Zymrael / PortHamiltonianNN
  Port-Hamiltonian Approach to Neural Network Training
  ☆22Updated 5 years ago
• AdrienCorenflos / jax-workshop
  JAX workshop for beginners
  ☆8Updated 2 months ago
• tgcsaba / GPSig
  Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
  ☆46Updated last year
• udellgroup / orie7191
  Materials for ORIE 7191: Topics in Optimization for Machine Learning
  ☆44Updated 5 years ago
• sklus / d3s
  Data-driven dynamical systems toolbox.
  ☆73Updated last month
• DiffEqML / torchcontrol
  A PyTorch library for all things nonlinear control and reinforcement learning.
  ☆45Updated 3 years ago
• pierresegonne / VINF
  Repository for DTU Special Course, focusing on Variational Inference using Normalizing Flows (VINF). Supervised by Michael Riis Andersen
  ☆25Updated 4 years ago
• luckystarufo / pySINDy
  SINDy (Sparse Identification of Nonlinear Dynamics) algorithms
  ☆74Updated 2 years ago
• IdahoLabResearch / BIhNNs
  The code enables to perform Bayesian inference in an efficient manner through the use of Hamiltonian Neural Networks (HNNs), Deep Neural …
  ☆14Updated 2 years ago
• cagatayyildiz / npode
  Learning unknown ODE models with Gaussian processes
  ☆26Updated 6 years ago
• DENG-MIT / StiffNet-Benchmark
  Benchmark for learning stiff problems using physics-informed machine learning
  ☆11Updated 3 years ago
• LRydin / KramersMoyal
  kramersmoyal: Kramers-Moyal coefficients for stochastic data of any dimension, to any desired order
  ☆72Updated 3 months ago