• HOME

  • OUR TEAM

  • RESEARCH

  • PUBLICATIONS

  • COURSES

  • More

    Use tab to navigate through the menu items.

    SCIENTIFIC COMPUTING and

    ARTIFICIAL INTELLIGENCE

    Home > Research >

    SEMINARS

    Weekly laboratory research seminars on research project development and critical review of literature in the interface of machine learning, deep learning and scientific computing.

    Boltzmann Machine; Undirected Graphical Models; Generative Models in Geology

    Undirected Graphical Models; Deep Learning for Fluid Mechanics

    Approximate Inference; Convolutional Neural Networks; Stein's Method

    Parallel approaches for the Bayesian Gaussian process latent variable model; Regularization in optim

    Simulation models for groundwater flow modeling; Cluster Expansion and Introduction to Alloy Theoret

    UQ in Groundwater Modeling, State Space Models

    A Review of Markov and hidden Markov Model, Regularization in Deep Learning

    Implementation of Neural Nets in PyTorch

    Deep Feedforward Networks, Bayesian Gaussian Process Latent Variable Model

    Implementation of Neural networks using PyTorch

    Sparse Gaussian Processes

    Expectation Propagation, Model uncertainty in RANS simulation, Variational Auto-Encoders

    Copyright © 2021 University of Notre Dame

    372 Fitzpatrick Hall, Notre Dame, IN 46556, USA

    Phone 574-631-2429   nzabaras@nd.edu

    Contact us

    Accessibility Information