Alternatives and similar repositories for Forecasting-Trajectory-of-Double-Pendulum

Users that are interested in Forecasting-Trajectory-of-Double-Pendulum are comparing it to the libraries listed below

• cagatayyildiz / ODE2VAE
  ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
  ☆130Updated last year
• mandubian / pytorch-neural-ode
  Experiment with Neural ODE on Pytorch
  ☆14Updated 6 years ago
• pvlachas / RNN-RC-Chaos
  RNN architectures trained with Backpropagation and Reservoir Computing (RC) methods for forecasting high-dimensional chaotic dynamical sy…
  ☆96Updated 2 years ago
• crisbodnar / ndp
  Official code for the ICLR 2021 paper Neural ODE Processes
  ☆75Updated 3 years ago
• a-norcliffe / sonode
  Experiments from the paper "On Second Order Behaviour in Augmented Neural ODEs"
  ☆60Updated last year
• DiffEqML / diffeqml-research
  This repository contains code released by DiffEqML Research
  ☆91Updated 3 years ago
• afqueiruga / StatefulOdeNets
  Refining continuous-in-depth neural networks
  ☆42Updated 3 years ago
• AaltoML / scalable-inference-in-sdes
  Methods and experiments for assumed density SDE approximations
  ☆12Updated 3 years ago
• erichson / koopmanAE
  Consistent Koopman Autoencoders
  ☆74Updated 2 years ago
• dgedon / DeepSSM_SysID
  Official PyTorch implementation of "Deep State Space Models for Nonlinear System Identification", 2020.
  ☆98Updated 3 years ago
• samholt / NeuralLaplace
  Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.
  ☆81Updated 6 months ago
• Yu-Group / adaptive-wavelets
  Adaptive, interpretable wavelets across domains (NeurIPS 2021)
  ☆81Updated 3 years ago
• helange23 / from_fourier_to_koopman
  Linear and non-linear spectral forecasting algorithms
  ☆137Updated 4 years ago
• CampusAI / Hamiltonian-Generative-Networks
  Re-implementation of Hamiltonian Generative Networks paper
  ☆33Updated 3 years ago
• yuan-yin / APHYNITY
  Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
  ☆48Updated last year
• tk-rusch / coRNN
  Official code for Coupled Oscillatory RNN (ICLR 2021, Oral)
  ☆50Updated 4 years ago
• ghliu / snopt
  Second-Order Neural ODE Optimizer, NeurIPS 2021 spotlight
  ☆55Updated 3 years ago
• mariosmat / hamiltonianNNetODEs
  Hamiltonian Neural Networks for solving Differential Equations
  ☆23Updated 3 years ago
• Physics-aware-AI / Symplectic-ODENet
  ☆47Updated 4 years ago
• zhengdao-chen / SRNN
  Symplectic Recurrent Neural Networks
  ☆28Updated 2 years ago
• DiffEqML / kairos
  Model hub for all your DiffeqML needs. Pretrained weights, modules, and basic inference infrastructure
  ☆27Updated 2 years ago
• pierresegonne / VINF
  Repository for DTU Special Course, focusing on Variational Inference using Normalizing Flows (VINF). Supervised by Michael Riis Andersen
  ☆25Updated 5 years ago
• pvlachas / RNN-Lyapunov-Spectrum
  A data-driven method to calculate the Lyapunov exponent of a dynamical system employing a GRU-RNN.
  ☆47Updated last year
• BorealisAI / continuous-time-flow-process
  PyTorch code of "Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows" (NeurIPS 2020)
  ☆48Updated 4 years ago
• TUM-ITR / koopcore
  Koopman Kernels for Learning Dynamical Systems from Trajectory Data
  ☆31Updated last year
• patrick-kidger / FasterNeuralDiffEq
  Code for "'Hey, that's not an ODE:' Faster ODE Adjoints via Seminorms" (ICML 2021)
  ☆88Updated 3 years ago
• fregu856 / ebms_regression
  Official implementation of "Energy-Based Models for Deep Probabilistic Regression" (ECCV 2020) and "How to Train Your Energy-Based Model …
  ☆105Updated 5 years ago
• YunzhuLi / CompositionalKoopmanOperators
  [ICLR 2020] Learning Compositional Koopman Operators for Model-Based Control
  ☆91Updated 4 years ago
• mbilos / neural-flows-experiments
  Experiments for Neural Flows paper
  ☆98Updated 3 years ago
• d-biswa / Symplectic-ODENet
  ☆15Updated 4 years ago