Search Results for author:
Found 9 papers, 3 papers with code
Geometric Neural Operators (GNPs) for Data-Driven Deep Learning of Non-Euclidean Operators
We introduce Geometric Neural Operators (GNPs) for accounting for geometric contributions in data-driven deep learning of operators.
SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic Dynamics
We introduce adversarial learning methods for data-driven generative modeling of the dynamics of $n^{th}$-order stochastic systems.
GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions
We develop data-driven methods incorporating geometric and topological information to learn parsimonious representations of nonlinear dynamics from observations.
MLMOD: Machine Learning Methods for Data-Driven Modeling in LAMMPS
MLMOD is a software package for incorporating machine learning approaches and models into simulations of microscale mechanics and molecular dynamics in LAMMPS.
Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems
We develop data-driven methods for incorporating physical information for priors to learn parsimonious representations of nonlinear systems arising from parameterized PDEs and mechanics.
GMLS-Nets: A framework for learning from unstructured data
Data fields sampled on irregularly spaced points arise in many applications in the sciences and engineering.
Coarse-Grained Methods for Heterogeneous Vesicles with Phase-Separated Domains: Elastic Mechanics of Shape Fluctuations, Plate Compression, and Channel Insertion
We develop coarse-grained particle approaches for studying the elastic mechanics of vesicles with heterogeneous membranes having phase-separated domains.
Soft Condensed Matter
Importance of the Mathematical Foundations of Machine Learning Methods for Scientific and Engineering Applications
There is a strong need for further mathematical developments on the foundations of machine learning methods to increase the level of rigor of employed methods and to ensure more reliable and interpretable results.
Bayesian Inference over the Stiefel Manifold via the Givens Representation
We show how the Givens representation can be used to develop practical methods for transforming densities over the Stiefel manifold into densities over subsets of Euclidean space.
