Scientific Computing and Artificial Intelligence
Scientific Computing and Artificial Intelligence (SCAI) lab; The intersection of computational physics and probabilistic machine learning.
Notre Dame, Indiana, U.S.
Pinned Repositories
ar-pde-cnn
Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs
bayesmultiscale
A Bayesian multiscale deep learning framework for flows in random media
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
deep-turbulence
Multi-fidelity Generative Deep Learning Turbulent Flows
GSVAE
Variational AutoEncoder using Graph Scattering
inn-surrogate
Solving inverse problems using conditional invertible neural networks.
MH-MDGM
MH-MDGM for Bayesian inverse problems
rans-uncertainty
Uncertainty Quantification of RANS Data-Driven Turbulence Modeling
transformer-physx
Transformers for modeling physical systems
Scientific Computing and Artificial Intelligence 's Repositories
zabaras/transformer-physx
Transformers for modeling physical systems
zabaras/deep-turbulence
Multi-fidelity Generative Deep Learning Turbulent Flows
zabaras/inn-surrogate
Solving inverse problems using conditional invertible neural networks.
zabaras/MH-MDGM
MH-MDGM for Bayesian inverse problems
zabaras/ar-pde-cnn
Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs
zabaras/CAAE-DRDCN-inverse
Deep residual networks for dimensionality reduction and surrogate modeling in high-dimensional inverse problems
zabaras/rans-uncertainty
Uncertainty Quantification of RANS Data-Driven Turbulence Modeling
zabaras/bayesmultiscale
A Bayesian multiscale deep learning framework for flows in random media
zabaras/GSVAE
Variational AutoEncoder using Graph Scattering
zabaras/cnn-inversion
Deep autoregressive neural networks for high-dimensional inverse problems
zabaras/cnn-surrogate
Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
zabaras/dcedn-gcs
Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multi-phase flow in heterogeneous random media
zabaras/pde-surrogate
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
zabaras/predictive-cvs
Predictive collective variable discovery with deep Bayesian models for atomistic systems.
zabaras/sgplvm-inverse
Experiments using the structured Bayesian Gaussian process latent variable model for inverse problems