Name: Scientific Computing and Artificial Intelligence
Type: Organization
Bio: Scientific Computing and Artificial Intelligence (SCAI) lab; The intersection of computational physics and probabilistic machine learning.
Location: Notre Dame, Indiana, U.S.
Blog: https://www.zabaras.com/
Scientific Computing and Artificial Intelligence 's Projects
Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs
A Bayesian multiscale deep learning framework for flows in random media
Deep residual networks for dimensionality reduction and surrogate modeling in high-dimensional inverse problems
Deep autoregressive neural networks for high-dimensional inverse problems
Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multi-phase flow in heterogeneous random media
Multi-fidelity Generative Deep Learning Turbulent Flows
Variational AutoEncoder using Graph Scattering
Solving inverse problems using conditional invertible neural networks.
MH-MDGM for Bayesian inverse problems
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
Predictive collective variable discovery with deep Bayesian models for atomistic systems.
Uncertainty Quantification of RANS Data-Driven Turbulence Modeling
Experiments using the structured Bayesian Gaussian process latent variable model for inverse problems
Transformers for modeling physical systems