dubourg / python-randomfields
A Python module that implements tools for the simulation and identification of random fields using the Karhunen-Loeve expansion representation.
☆22Updated 8 years ago
Alternatives and similar repositories for python-randomfields:
Users that are interested in python-randomfields are comparing it to the libraries listed below
- Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations☆67Updated 4 years ago
- ☆47Updated last year
- Code for the paper: Solving and Learning Nonlinear PDEs with Gaussian Processes☆39Updated 6 months ago
- Variational Neural Networks for the Solution of Partial Differential Equations☆8Updated 5 years ago
- ☆41Updated 2 years ago
- ☆11Updated 2 years ago
- ☆62Updated 5 years ago
- Dimension reduced surrogate construction for parametric PDE maps☆37Updated 3 weeks ago
- Dimensionless learning codes for our paper called "Data-driven discovery of dimensionless numbers and governing laws from scarce measurem…☆37Updated 10 months ago
- ☆10Updated 3 years ago
- ☆13Updated 5 years ago
- ☆24Updated 6 years ago
- Sparsity-promoting Kernel Dynamic Mode Decomposition for Nonlinear Dynamical Systems☆29Updated 2 years ago
- Pseudospectral Kolmogorov Flow Solver☆38Updated last year
- Practicum on Supervised Learning in Function Spaces☆33Updated 3 years ago
- combination of sparse identification of nonlinear dynamics with Akaike information criteria☆16Updated 7 years ago
- TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks…☆20Updated 2 years ago
- Machine learning of linear differential equations using Gaussian processes☆24Updated 6 years ago
- The VECMA toolkit for creating surrogate models of multiscale systems.☆19Updated 3 months ago
- GPTIPS2F: Symbolic Regression toolbox for MATLAB evolved☆11Updated 2 years ago
- Multifidelity Kriging, Efficient Global Optimization☆18Updated 6 years ago
- 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
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆48Updated 4 years ago
- Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning☆17Updated last year
- Multi-fidelity reduced-order surrogate modeling☆21Updated 4 months ago
- Sparse Physics-based and Interpretable Neural Networks☆48Updated 3 years ago
- Multi Fidelity Monte Carlo☆25Updated 5 years ago
- Code for the paper "Structure-preserving neural networks" published in Journal of Computational Physics (JCP).☆19Updated last year
- Generator of 2D gaussian random fields☆50Updated 2 years ago
- ☆32Updated last month