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02/07/2023
SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic Dynamics
We introduce adversarial learning methods for data-driven generative mod...
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12/20/2022
Incorporating Shear into Stochastic Eulerian Lagrangian Methods for Rheological Studies of Complex Fluids and Soft Materials
We develop computational methods that incorporate shear into fluctuating...
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06/10/2022
GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions
We develop data-driven methods incorporating geometric and topological i...
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10/02/2021
Drift-Diffusion Dynamics and Phase Separation in Curved Cell Membranes and Dendritic Spines: Hybrid Discrete-Continuum Methods
We develop methods for investigating protein drift-diffusion dynamics in...
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07/29/2021
MLMOD Package: Machine Learning Methods for Data-Driven Modeling in LAMMPS
We discuss a software package for incorporating into simulations data-dr...
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12/07/2020
Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems
We develop data-driven methods for incorporating physical information fo...
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09/07/2019
GMLS-Nets: A framework for learning from unstructured data
Data fields sampled on irregularly spaced points arise in many applicati...
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06/04/2019
Surface Fluctuating Hydrodynamics Methods for the Drift-Diffusion Dynamics of Particles and Microstructures within Curved Fluid Interfaces
We introduce fluctuating hydrodynamics approaches on surfaces for captur...
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08/07/2018
Importance of the Mathematical Foundations of Machine Learning Methods for Scientific and Engineering Applications
There has been a lot of recent interest in adopting machine learning met...
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10/25/2017