sayin / Data_Driven_Symbolic_RegressionLinks
Interpretable machine learning (symbolic regression) using Genetic programming/Gene expression programming and Sparse regression used to identify physical process, numerical schemes, and LES subgrid scale (eddy viscosity) turbulence models.
☆33Updated 4 years ago
Alternatives and similar repositories for Data_Driven_Symbolic_Regression
Users that are interested in Data_Driven_Symbolic_Regression are comparing it to the libraries listed below
Sorting:
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.☆26Updated 3 years ago
- ☆47Updated last year
- The unsupervised learning problem trains a diffeomorphic spatio-temporal grid, that registers the output sequence of the PDEs onto a non-…☆19Updated 2 years ago
- A Deep Learning based Approach to Reduced Order Modeling for Turbulent Flow Control using LSTM Neural Networks. arXiv:1804.09269☆43Updated 7 years ago
- ☆21Updated 4 years ago
- Methods and code for J. L. Callaham, J. N. Kutz, B. W. Brunton, and S. L. Brunton (2020)☆56Updated 4 years ago
- Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.☆24Updated 3 years ago
- Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics☆59Updated 3 years ago
- Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton☆25Updated 3 years ago
- Python script solving the Burgers' equation (équation de Burgers) 1D by using FFT pseudo-spectral method.☆26Updated 3 years ago
- Source code for the paper "Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process" …☆32Updated 2 years ago
- ☆19Updated 3 years ago
- ☆18Updated last year
- Exploit Auto-encoder for exploring and predict flow dynamic☆10Updated 5 years ago
- ☆18Updated 4 years ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆49Updated 4 years ago
- Dimensionless learning codes for our paper called "Data-driven discovery of dimensionless numbers and governing laws from scarce measurem…☆37Updated last year
- Python codes for Locally Adaptive Activation Function (LAAF) used in deep neural networks. Please cite this work as "A D Jagtap, K Kawa…☆40Updated 2 years ago
- Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations☆69Updated 5 years ago
- This codes calculates the dimensionalized POD and uses SINDy from the PySINDy python package to build a data-driven model for it. The cod…☆20Updated 4 years ago
- Code to accompany the paper "Discovery of Physics from Data: Universal Laws and Discrepancies"☆27Updated 5 years ago
- Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems☆63Updated 5 years ago
- Symbolic genetic algorithm for discovering open-form partial differential equations☆40Updated 3 years ago
- Supporting code for "Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders"☆21Updated 4 years ago
- This repository contains the files used in the paper " Reduced-order Model for Fluid Flows via Neural Ordinary Differential Equations"☆19Updated 2 years ago
- Pytorch implementation of the DeepMoD algorithm: [arXiv:1904.09406]☆32Updated last year
- A MATLAB package for computing the optimized dynamic mode decomposition (DMD)☆18Updated 6 years ago
- Code repository for "Learned Turbulence Modelling with Differentiable Fluid Solvers"☆38Updated 2 years ago
- ☆21Updated 4 years ago
- This repository contains the simple source codes of "Convolutional neural network and long short-term memory based reduced order surrogat…☆13Updated 4 years ago