PredictiveScienceLab / inverse-bgo
Use Bayesian Global Optimization to solve inverse problems
☆13Updated 9 years ago
Related projects ⓘ
Alternatives and complementary repositories for inverse-bgo
- Deep Learning application to the partial differential equations☆29Updated 6 years ago
- Parametric Gaussian Process Regression for Big Data☆44Updated 4 years ago
- ☆25Updated 6 years ago
- Multi-Information Source Optimization☆22Updated 5 years ago
- sparse dynamic mode decomposition in python☆36Updated 8 years ago
- A Sensitivity and uncertainty analysis toolbox for Python based on the generalized polynomial chaos method☆76Updated last week
- Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations☆66Updated 4 years ago
- Bayesian Dynamic Mode Decomposition (Bayesian DMD)☆18Updated 2 years ago
- Solving stochastic differential equations and Kolmogorov equations by means of deep learning and Multilevel Monte Carlo simulation☆10Updated 3 years ago
- Source code of: "Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models".☆37Updated 2 years ago
- Machine learning algorithms for discovering dimensionless groups from simulation and experimental data☆11Updated 2 years ago
- Machine learning of linear differential equations using Gaussian processes☆22Updated 6 years ago
- Optimal Transport for Dummies - Code, slides and article☆32Updated 7 years ago
- Code repository for the paper "Learning partial differential equations for biological transport models from noisy spatiotemporal data"☆10Updated 5 years ago
- A Python module that implements tools for the simulation and identification of random fields using the Karhunen-Loeve expansion represent…☆20Updated 8 years ago
- A pyTorch Extension for Applied Mathematics☆38Updated 4 years ago
- Sample code for the NIPS paper "Scalable Variational Inference for Dynamical Systems"☆26Updated 5 years ago
- Implementation for Non-stationary Spectral Kernels (NIPS 2017)☆20Updated 4 years ago
- Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton☆24Updated 2 years ago
- Parametric Gaussian Process Regression for Big Data (Matlab Version)☆24Updated 6 years ago
- Simulation-Enabled Prediction, Inference, and Analysis: physics-informed statistical learning.☆33Updated last year
- A MATLAB package for computing the optimized dynamic mode decomposition (DMD)☆17Updated 6 years ago
- ☆19Updated 4 years ago
- A hands-on tutorial on supervised learning with Gaussian processes☆36Updated 4 years ago
- Dynamic Mode Decomposition☆57Updated 7 years ago
- Python and MATLAB code for Stein Variational sampling methods☆23Updated 5 years ago
- Course notes for graduate-level class on numerical methods for deep learning☆49Updated 3 years ago
- Multi Fidelity Monte Carlo☆24Updated 4 years ago
- From the paper "Dynamic mode decomposition for multiscale nonlinear physics" by Dylewsky, Tao, & Kutz☆9Updated 4 years ago
- A repository that contains scripts to replicate results in the Deep UQ paper.☆11Updated 6 years ago