Pinned Repositories
ar-pde-cnn
Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs
CAAE-DRDCN-inverse
Deep residual networks for dimensionality reduction and surrogate modeling in high-dimensional inverse problems
cnn-inversion
Deep autoregressive neural networks for high-dimensional inverse problems
cnn-surrogate
Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
dcedn-gcs
Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multi-phase flow in heterogeneous random media
gptorch
Gaussian processes with PyTorch
pde-surrogate
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
rans-uncertainty
Uncertainty Quantification of RANS Data-Driven Turbulence Modeling
sgplvm-inverse
Experiments using the structured Bayesian Gaussian process latent variable model for inverse problems
structured-gpflow
Gaussian process models with structured inputs based on GPflow
Center for Informatics and Computational Science's Repositories
cics-nd/pde-surrogate
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
cics-nd/cnn-surrogate
Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
cics-nd/ar-pde-cnn
Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs
cics-nd/rans-uncertainty
Uncertainty Quantification of RANS Data-Driven Turbulence Modeling
cics-nd/gptorch
Gaussian processes with PyTorch
cics-nd/cnn-inversion
Deep autoregressive neural networks for high-dimensional inverse problems
cics-nd/dcedn-gcs
Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multi-phase flow in heterogeneous random media
cics-nd/CAAE-DRDCN-inverse
Deep residual networks for dimensionality reduction and surrogate modeling in high-dimensional inverse problems
cics-nd/structured-gpflow
Gaussian process models with structured inputs based on GPflow
cics-nd/sgplvm-inverse
Experiments using the structured Bayesian Gaussian process latent variable model for inverse problems
cics-nd/predictive-cvs
Predictive collective variable discovery with deep Bayesian models for atomistic systems.
cics-nd/sgp-experiments
Experiments using the structured GP, GP-LVM, and warped GP.