Alternatives and similar repositories for ctf-dl-tutorial

Users that are interested in ctf-dl-tutorial are comparing it to the libraries listed below

• PredictiveIntelligenceLab / NOMAD
  ☆16Updated last year
• csccm-iitd / VB-DeepONet
  ☆13Updated 3 years ago
• benmoseley / symbolic-regression-with-deep-neural-networks-workshop
  Introductory workshop on using deep neural networks for symbolic regression
  ☆15Updated 9 months ago
• ParamIntelligence / Awesome-Scientific-Foundation-Models
  ☆28Updated 4 months ago
• jtoleary / SPINN
  Stochastic Physics-Informed Neural Networks: A Moment-Matching Framework for Learning Hidden Physics within Stochastic Differential Equat…
  ☆14Updated 4 years ago
• elizqian / operator-inference
  ☆10Updated 2 years ago
• MatteoTomasetto / SHRED-ROM
  Reduced order modeling with shallow recurrent decoder networks
  ☆19Updated last week
• PredictiveIntelligenceLab / BayesianDifferentiableProgramming
  ☆21Updated 5 years ago
• mariodeflorio / AI-Lorenz
  ☆35Updated 2 years ago
• PredictiveIntelligenceLab / UQDeepONet
  ☆10Updated 2 years ago
• delta-lab-ai / pde-controller
  ☆24Updated 7 months ago
• rmojgani / PhysicsAwareAE
  The unsupervised learning problem trains a diffeomorphic spatio-temporal grid, that registers the output sequence of the PDEs onto a non-…
  ☆20Updated 3 years ago
• pehersto / ng
  Neural Galerkin
  ☆16Updated 2 years ago
• josephbakarji / bucki-data
  Machine learning algorithms for discovering dimensionless groups from simulation and experimental data
  ☆15Updated 3 years ago
• pde-sim / teng
  Official Code for ICML 2024 paper "TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision"
  ☆18Updated last year
• cselab / G-LED
  Generative Learning for Forecasting the Dynamics of High Dimensional Complex Systems
  ☆40Updated 10 months ago
• cianmscannell / pinns
  Physics-informed neural networks
  ☆16Updated 5 years ago
• ASK-Berkeley / physics-NNs-hard-constraints
  [ICLR 2024] Scaling physics-informed hard constraints with mixture-of-experts.
  ☆37Updated last year
• briandesilva / discovery-of-physics-from-data
  Code to accompany the paper "Discovery of Physics from Data: Universal Laws and Discrepancies"
  ☆28Updated 5 years ago
• FrancescoRegazzoni / LDNets
  ☆63Updated last year
• mpimd-csc / RK4-SinDy
  ☆14Updated 4 years ago
• PredictiveIntelligenceLab / LOCA
  ☆15Updated 2 years ago
• MaxGhi8 / HyperNOs
  ☆23Updated 2 weeks ago
• dynamicslab / modified-SINDy
  Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…
  ☆61Updated 3 years ago
• gaoliyao / BayesianSindyAutoencoder
  Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants. Proceedings of the Royal S…
  ☆11Updated last year
• maziarraissi / MultistepNNs
  Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems
  ☆63Updated 5 years ago
• CognitiveModeling / finn
  The public repository about our joint FINN research project
  ☆38Updated 3 years ago
• kpchamp / SINDySR3
  ☆30Updated 5 years ago
• FilippoMB / Physics-Informed-Neural-Networks-tutorial
  Hands-on tutorial for implementing Physics Informed Neural Networks in Pytorch
  ☆58Updated 9 months ago
• benmoseley / intro-to-jax-workshop
  Introduction to JAX Workshop @ ETH Zurich, 25 June 2024
  ☆39Updated 9 months ago