Alternatives and similar repositories for aipoincare

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

• dynamicslab / langevin-regression
  Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton
  ☆26Updated 3 years ago
• hongyehu / RG-Flow
  This is project page for the paper "RG-Flow: a hierarchical and explainable flow model based on renormalization group and sparse prior". …
  ☆88Updated 3 years ago
• lorenzrichter / PDE-backward-solver
  ☆23Updated last year
• NBoulle / RationalNets
  Code for the paper "Rational neural networks", NeurIPS 2020
  ☆29Updated 4 years ago
• pooyasf / DGM
  Solving High Dimensional Partial Differential Equations with Deep Neural Networks
  ☆34Updated 4 years ago
• NBoulle / greenlearning
  Learning Green's functions of partial differential equations with deep learning.
  ☆71Updated last year
• omron-sinicx / srsd-benchmark
  [DMLR] Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
  ☆37Updated last year
• luckystarufo / multiscale_HiTS
  ☆73Updated 5 years ago
• QMCSoftware / QMCSoftware
  Quasi-Monte Carlo point generators, automatic transformations, and adaptive stopping criteria
  ☆77Updated this week
• mitmath / 18336
  18.336 - Fast Methods for Partial Differential and Integral Equations
  ☆187Updated last year
• tonyshardlow / PICSPDE
  Python codes for Introduction to Computational Stochastic PDE
  ☆47Updated 2 weeks ago
• lsarra / rmi
  Code to estimate Renormalized Mutual Information in simple settings
  ☆13Updated 4 years ago
• samuelkim314 / DeepSymRegTorch
  PyTorch implementation of the EQL network, a neural network for symbolic regression
  ☆42Updated 4 years ago
• xwinxu / bayeSDE
  Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"
  ☆171Updated 3 years ago
• facebookresearch / neural-conservation-law
  code for "Neural Conservation Laws A Divergence-Free Perspective".
  ☆41Updated 3 years ago
• LRydin / KramersMoyal
  kramersmoyal: Kramers-Moyal coefficients for stochastic data of any dimension, to any desired order
  ☆75Updated last year
• brandstetter-johannes / LPSDA
  Repo to the paper "Lie Point Symmetry Data Augmentation for Neural PDE Solvers"
  ☆56Updated 2 years ago
• afqueiruga / StatefulOdeNets
  Refining continuous-in-depth neural networks
  ☆42Updated 4 years ago
• henry1jin / eigeNN
  ☆12Updated 5 years ago
• PhIMaL / temporal_normalizing_flows
  ☆11Updated 4 years ago
• mfinzi / constrained-hamiltonian-neural-networks
  ☆112Updated 4 years ago
• li012589 / NeuralRG
  Pytorch source code for arXiv paper Neural Network Renormalization Group, a generative model using variational renormalization group and …
  ☆80Updated 6 years ago
• eth-nn-physics / nn_physical_concepts
  ☆175Updated 2 years 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 4 years ago
• mattja / sdeint
  Numerical integration of Ito or Stratonovich SDEs
  ☆167Updated 2 years ago
• hftsoi / symbolfit
  Automatic parametric modeling with symbolic regression
  ☆61Updated 5 months ago
• RMT-TheoryAndPractice / RMT
  ☆126Updated 8 years ago
• samuelkim314 / DeepSymReg
  Official repository for the paper "Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery"
  ☆76Updated 2 years ago
• zhengdao-chen / SRNN
  Symplectic Recurrent Neural Networks
  ☆28Updated 2 years ago
• wdphy16 / stat-mech-van
  Code for 'Solving Statistical Mechanics using Variational Autoregressive Networks'.
  ☆98Updated 7 months ago