Alternatives and similar repositories for symbolic-regression-with-deep-neural-networks-workshop

Users that are interested in symbolic-regression-with-deep-neural-networks-workshop are comparing it to the libraries listed below

• kopanicakova / HINTS_precond
  This repository contains code, which was used to generate large-scale results in the HINTS paper.
  ☆35Updated last year
• jayroxis / qres
  Learning with Higher Expressive Power than Neural Networks (On Learning PDEs)
  ☆16Updated 4 years ago
• tum-pbs / StableBPTT
  The source code the for the ICLR'24 paper "Stabilizing Backpropagation Through Time to Learn Complex Physics"
  ☆11Updated last year
• eagle-dataset / EagleMeshTransformer
  Code for Mesh Transformer describes in the EAGLE dataset
  ☆42Updated 10 months ago
• benmoseley / intro-to-jax-workshop
  Introduction to JAX Workshop @ ETH Zurich, 25 June 2024
  ☆39Updated 9 months ago
• csccm-iitd / VB-DeepONet
  ☆13Updated 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
• PredictiveIntelligenceLab / ImprovedDeepONets
  ☆30Updated 3 years ago
• neuraloperator / cond-diffusion-operators-edm
  ☆19Updated last year
• dmusekamp / al4pde
  AL4PDE: A Benchmark for Active Learning for Neural PDE Solvers
  ☆28Updated 7 months ago
• JohnCSu / DeepONet_Pytorch_Demo
  Simple demo on implementing data driven and physics informed Deep O Nets in pytorch
  ☆19Updated last year
• dynamicslab / ctf-dl-tutorial
  CTF workshop deep learning tutorial and hackathon
  ☆13Updated 2 years ago
• HewlettPackard / separable-operator-networks
  Official repo for separable operator networks -- extreme-scale operator learning for parametric PDEs.
  ☆38Updated last year
• PredictiveIntelligenceLab / NOMAD
  ☆16Updated last year
• merantix-momentum / gnn-bvp-solver
  Source code for paper "Learning the Solution Operator of Boundary Value Problems using Graph Neural Networks"
  ☆19Updated last year
• PredictiveIntelligenceLab / LOCA
  ☆15Updated 2 years ago
• tum-pbs / unrolling
  ☆18Updated last year
• mia-jinns / jinns
  Physics Informed Neural Networks (PINNs) + SPINNs + HyperPINNs + Adaptative Loss Weights with JAX 📓 Check out our various notebooks to g…
  ☆43Updated last week
• SmartTURB / diffusion-lagr
  Synthetic Lagrangian Turbulence by Generative Diffusion Models
  ☆26Updated last year
• cianmscannell / pinns
  Physics-informed neural networks
  ☆16Updated 5 years ago
• rezaakb / pinns-jax
  PINNs-JAX, Physics-informed Neural Networks (PINNs) implemented in JAX.
  ☆58Updated last year
• atzberg / geo_neural_op
  Geometric Neural Operators (GNPs) for machine learning tasks on point-cloud representations: curvature estimation, shape deformations, so…
  ☆31Updated last month
• tianshao1992 / DENO4pytorch
  Differential equation neural operator
  ☆22Updated 2 years ago
• jtoleary / SPINN
  Stochastic Physics-Informed Neural Networks: A Moment-Matching Framework for Learning Hidden Physics within Stochastic Differential Equat…
  ☆14Updated 4 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"
  ☆16Updated last year
• xgxgnpu / My-awesome-PINN-papers
  ☆36Updated 3 years ago
• peterchencyc / CROM_online_deployment
  ☆15Updated 2 years ago
• ASK-Berkeley / Neural-Spectral-Methods
  [ICLR 2024] Neural Spectral Methods: Self-supervised learning in the spectral domain.
  ☆48Updated last year
• tum-pbs / apebench
  [Neurips 2024] A benchmark suite for autoregressive neural emulation of PDEs. (≥46 PDEs in 1D, 2D, 3D; Differentiable Physics; Unrolled T…
  ☆95Updated 2 months ago
• dynamicslab / modified-SINDy
  Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…
  ☆60Updated 3 years ago