gpschool / labsLinks
Repository for labs
☆15Updated 11 months ago
Alternatives and similar repositories for labs
Users that are interested in labs are comparing it to the libraries listed below
Sorting:
- Spring 2023 seminar on automated experiment☆23Updated 2 years ago
- Gaussian Processes for Experimental Sciences☆227Updated last month
- Python package 'dgpsi' for deep and linked Gaussian process emulations☆27Updated last week
- Flexible and efficient tools for high-dimensional approximation, scientific machine learning and uncertainty quantification.☆64Updated this week
- Introduction to Uncertainty Quantification☆252Updated 3 years ago
- FoKL-GP implements Karhunen-Loève decomposed Gaussian processes with built-in forward variable selection. Decomposed GPs are key to embed…☆18Updated last week
- Fast Bayesian optimization, quadrature, inference over arbitrary domain with GPU parallel acceleration☆28Updated last week
- Materials for ACerS Automated Experiment Course☆20Updated last year
- Gaussian process augmented with a probabilistic model of expected system's behavior☆15Updated 3 years ago
- Python Library for Generalized Gaussian Process Modeling☆25Updated 4 months ago
- Bayesian neural networks via MCMC: tutorial☆58Updated 9 months ago
- Design of experiments for Python☆160Updated last year
- A software package for flexible HPC GPs☆16Updated last month
- ME 539 - Introduction to Scientific Machine Learning☆121Updated 3 weeks ago
- Heteroscedastic Bayesian Optimisation in Numpy☆21Updated 2 years ago
- ☆26Updated 6 months ago
- Fully and Partially Bayesian Neural Nets☆75Updated 3 months ago
- A framework for Bayesian model selection (BMS) and Bayesian model Averaging (BMA).☆42Updated last year
- Mixed Variable Multi-Objective Optimisation☆19Updated 4 years ago
- List of papers on robust Bayesian optimization☆17Updated 3 years ago
- ☆35Updated 2 months ago
- Python library for parallel multiobjective simulation optimization☆82Updated 11 months ago
- ☆13Updated 2 years ago
- kramersmoyal: Kramers-Moyal coefficients for stochastic data of any dimension, to any desired order☆74Updated 7 months ago
- Physics informed Bayesian network + autoencoder for matching process / variable / performance in solar cells.☆32Updated 3 years ago
- The materials for the Spring Mathematics in Materials course at the UTK MSE☆49Updated last year
- Ternary plots as projections of Matplotlib☆50Updated last year
- PDE-VAE: Variational Autoencoder for Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning☆35Updated 3 years ago
- A demo code for implementation of differentiable thermodynamic modeling in JAX.☆10Updated 3 years ago
- The code enables to perform Bayesian inference in an efficient manner through the use of Hamiltonian Neural Networks (HNNs), Deep Neural …☆15Updated 2 years ago