The following are links to software developed by members, affiliate members, or students of the Center with the intent to provide unlimited open access to resources produced in line with the Center's mission.
- Deep autoregressive neural networks for dynamical solute transport system and high-dimensional inverse problems
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
- Gaussian process models with structured inputs extending GPflow
- Experiments using the structured Bayesian Gaussian process latent variable model for inverse problems
- Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media
- Turbulence Modeling with Bayesian Deep Neural Networks