169 citations · 837 across the 31 of their papers we have counts for
5 papers · 1 filter
Geometric Neural Operators (GNPs) for Data-Driven Deep Learning of Non-Euclidean Operators
Blaine Quackenbush, Paul J. Atzberger
We introduce Geometric Neural Operators (GNPs) for accounting for geometric contributions in data-driven deep learning of operators. We show how GNPs can be used (i) to estimate ge…
SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic Dynamics
Panos Stinis, Constantinos Daskalakis, Paul J. Atzberger
We introduce adversarial learning methods for data-driven generative modeling of the dynamics of -order stochastic systems. Our approach builds on Generative Adversarial Ne…
GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions
Ryan Lopez, Paul J. Atzberger
We develop data-driven methods incorporating geometric and topological information to learn parsimonious representations of nonlinear dynamics from observations. The approaches lea…
MLMOD: Machine Learning Methods for Data-Driven Modeling in LAMMPS
Paul J. Atzberger
MLMOD is a software package for incorporating machine learning approaches and models into simulations of microscale mechanics and molecular dynamics in LAMMPS. Recent machine learn…
GMLS-Nets: A framework for learning from unstructured data
Nathaniel Trask, Ravi G. Patel, Ben J. Gross +1
Data fields sampled on irregularly spaced points arise in many applications in the sciences and engineering. For regular grids, Convolutional Neural Networks (CNNs) have been succe…