maziarraissi / ParametricGPView external linksLinks
Parametric Gaussian Process Regression for Big Data
☆45Feb 20, 2020Updated 5 years ago
Alternatives and similar repositories for ParametricGP
Users that are interested in ParametricGP are comparing it to the libraries listed below
Sorting:
- Machine learning of linear differential equations using Gaussian processes☆26May 2, 2018Updated 7 years ago
- Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations☆69May 26, 2020Updated 5 years ago
- Tutorial on a number of topics in Deep Learning☆37Feb 20, 2020Updated 5 years ago
- Tutorial on Gaussian Processes☆65Feb 20, 2020Updated 5 years ago
- Deep Learning of Turbulent Scalar Mixing☆17Oct 6, 2019Updated 6 years ago
- Introduction to Machine Learning in R☆23May 7, 2021Updated 4 years ago
- Hidden physics models: Machine learning of nonlinear partial differential equations☆149Feb 20, 2020Updated 5 years ago
- Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems☆63Feb 20, 2020Updated 5 years ago
- Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations☆156Feb 20, 2020Updated 5 years ago
- Backpropagation in Python, C++, and Cuda☆47Feb 17, 2020Updated 6 years ago
- ☆11Mar 31, 2021Updated 4 years ago
- ☆12Jan 29, 2025Updated last year
- Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations☆285Jul 30, 2022Updated 3 years ago
- Sparse Identification of Nonlinear Dynamics for Hybrid Systems☆28Aug 8, 2018Updated 7 years ago
- Bayesian System IDentification☆28Jan 24, 2018Updated 8 years ago
- ☆30Jun 7, 2018Updated 7 years ago
- ☆15Nov 18, 2024Updated last year
- combination of sparse identification of nonlinear dynamics with Akaike information criteria☆16Aug 19, 2017Updated 8 years ago
- Sparse Identification of Nonlinear Dynamics for Boundary Value Problems☆13Apr 10, 2021Updated 4 years ago
- Using Machine Learning to Predict Extreme Events in Complex Systems☆14Dec 15, 2019Updated 6 years ago
- the proper orthogonal decomposition(POD) and dynamic mode decomposition(DMD) methods☆17Jul 24, 2019Updated 6 years ago
- ☆63Jul 24, 2019Updated 6 years ago
- Learning dynamical systems from data: Koopman☆18Feb 3, 2020Updated 6 years ago
- Karhunen-Loeve Expansions for Gaussian Random Fields in Python☆17Mar 24, 2014Updated 11 years ago
- LES-ML closures for Kraichnan turbulence☆19Feb 3, 2020Updated 6 years ago
- Scalable Log Determinants for Gaussian Process Kernel Learning (https://arxiv.org/abs/1711.03481) (NIPS 2017)☆18Nov 10, 2017Updated 8 years ago
- Implementation of Gibbs sampling for spike and slab priors☆18Mar 1, 2017Updated 8 years ago
- ☆12Aug 22, 2025Updated 5 months ago
- ☆40Sep 11, 2023Updated 2 years ago
- This code implements the Tensor Basis Neural Network (TBNN) as described in Ling et al. (Journal of Fluid Mechanics, 2016).☆44Feb 10, 2018Updated 8 years ago
- Invariant Information Clustering for Unsupervised Image Classification and Segmentation☆16Jul 19, 2019Updated 6 years ago
- Probabilistic Response mOdel Fitting with Interactive Tools☆15Jan 19, 2026Updated 3 weeks ago
- This repository contains the simple source codes of "Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies…☆17May 14, 2021Updated 4 years ago
- A Python toolkit to calculate and visualize turbulence anisotropy and turbulent viscosity from Reynolds stress tensor data.☆21Dec 22, 2023Updated 2 years ago
- Main component extraction for outlier detection☆20Dec 6, 2020Updated 5 years ago
- ☆25Jun 12, 2025Updated 8 months ago
- ML-based turbulence modeling for astrophysics☆14Aug 18, 2023Updated 2 years ago
- 1D RANS model simulation at fully developed turbulent channel flow.☆19Jan 20, 2017Updated 9 years ago
- A code for fast, massively-parallel direct numerical simulations (DNS) of canonical flows☆16Sep 24, 2021Updated 4 years ago