Alternatives and similar repositories for JSD-SBI

Users that are interested in JSD-SBI are comparing it to the libraries listed below

• MarcusHA94 / structural-dynamics-pinns
  Research/development of physics-informed neural networks for dynamic systems
  ☆27Updated 8 months ago
• JayLago / DLDMD
  Deep learning assisted dynamic mode decomposition
  ☆19Updated 3 years ago
• ETH-WindMil / Sonkyo-Benchmark
  Sonkyo blade benchmark
  ☆18Updated 4 years ago
• mathLab / PyDMD
  mathLab mirror of Python Dynamic Mode Decomposition
  ☆98Updated 4 months ago
• haller-group / SSMLearn
  Data-driven reduced order modeling for nonlinear dynamical systems
  ☆84Updated last month
• VVeida / RK4_PINN
  ☆12Updated last year
• baddoo / LANDO
  Codes for Linear and Nonlinear Disambiguation Optimization (LANDO)
  ☆29Updated 3 years ago
• sethhirsh / BayesianSindy
  ☆14Updated 3 years ago
• liouvill / PgDMM
  Physics-guided Deep Markov Models
  ☆12Updated 3 years ago
• LivingMatterLab / xPINNs
  when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…
  ☆73Updated 3 years ago
• giuliamesc / BPINNs
  ☆31Updated 2 years ago
• iqiukp / Gaussian-Process-Regression
  Gaussian Process Regression using GPML toolbox
  ☆35Updated 5 years ago
• katiana22 / GDM-PCE
  Source code of: "Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models".
  ☆37Updated 3 years ago
• matlab-deep-learning / Inverse-Problems-using-Physics-Informed-Neural-Networks-PINNs
  ☆79Updated last year
• JRice15 / physics-informed-autoencoders
  Research project conducted at Pacific Northwest National Laboratory, exploring the use of physics-informed autoencoders to predict fluid …
  ☆35Updated 2 years ago
• junsebas97 / modeling_structural_hysteresis_with_PGUODEs
  This repository stores the implementation of three hysteretic models that use Physics-Guided neural networks (PGNNs) and Universal ordina…
  ☆8Updated 2 years ago
• zhry10 / PhyLSTM
  We introduce an innovative physics-informed LSTM framework for metamodeling of nonlinear structural systems with scarce data.
  ☆92Updated last year
• PML-UCF / pinn_ode_tutorial
  ☆129Updated 3 years ago
• opaliss / dmd_autoencoder
  Enhancing Dynamic Mode Decomposition using Autoencoder Networks.
  ☆30Updated 4 years ago
• diegoandresalvarez / BWBN_TMCMC
  Identification of Bouc–Wen type models using the transitional Markov chain Monte Carlo method
  ☆12Updated 4 months ago
• pswpswpsw / SKDMD
  Sparsity-promoting Kernel Dynamic Mode Decomposition for Nonlinear Dynamical Systems
  ☆30Updated 2 years ago
• haozhg / odmd
  AI4Science: Python/Matlab implementation of online and window dynamic mode decomposition (Online DMD and Window DMD)
  ☆43Updated 2 years ago
• XuhuiM / Multi-Fidelity-DNNs-PINNs
  ☆37Updated last year
• NREL / phygnn
  physics-guided neural networks (phygnn)
  ☆93Updated last month
• urban-fasel / EnsembleSINDy
  ☆22Updated 3 years ago
• ms140429 / PIML_Converter
  This is the code and data for the IEEE TPEL paper "Parameter Estimation of Power Electronic Converters with Physics-informed Machine Lear…
  ☆64Updated 8 months ago
• isds-neu / PiSL
  A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.
  ☆26Updated 3 years ago
• ContiPaolo / MultiFidelity_NNs
  Multi-fidelity regression with neural networks
  ☆13Updated 7 months ago
• ZhaoChenCivilSciML / EQDiscovery-1
  Physics-informed learning of governing equations from scarce data
  ☆11Updated 4 years ago
• maziarraissi / TutorialGP
  Tutorial on Gaussian Processes
  ☆62Updated 5 years ago