Paul J. Atzberger

Orcid: 0000-0001-6806-8069

According to our database1, Paul J. Atzberger authored at least 28 papers between 2007 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
MLMOD: Machine Learning Methods for Data-Driven Modeling in LAMMPS.
J. Open Source Softw., October, 2023

Coarse-grained methods for heterogeneous vesicles with phase-separated domains: Elastic mechanics of shape fluctuations, plate compression, and channel insertion.
Math. Comput. Simul., 2023

SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic Dynamics.
CoRR, 2023

2022
Surface fluctuating hydrodynamics methods for the drift-diffusion dynamics of particles and microstructures within curved fluid interfaces.
J. Comput. Phys., 2022

First-passage time statistics on surfaces of general shape: Surface PDE solvers using Generalized Moving Least Squares (GMLS).
J. Comput. Phys., 2022

Incorporating Shear into Stochastic Eulerian Lagrangian Methods for Rheological Studies of Complex Fluids and Soft Materials.
CoRR, 2022

Magnus Exponential Integrators for Stiff Time-Varying Stochastic Systems.
CoRR, 2022

GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions.
CoRR, 2022

2021
Drift-Diffusion Dynamics and Phase Separation in Curved Cell Membranes and Dendritic Spines: Hybrid Discrete-Continuum Methods.
CoRR, 2021

MLMOD Package: Machine Learning Methods for Data-Driven Modeling in LAMMPS.
CoRR, 2021

Variational Autoencoders for Learning Nonlinear Dynamics of PDEs and Reductions.
Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to, 2021

2020
Meshfree methods on manifolds for hydrodynamic flows on curved surfaces: A Generalized Moving Least-Squares (GMLS) approach.
J. Comput. Phys., 2020

Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems.
CoRR, 2020

GMLS-Nets: A Machine Learning Framework for Unstructured Data.
Proceedings of the AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 23rd - to, 2020

2019
GMLS-Nets: A framework for learning from unstructured data.
CoRR, 2019

2018
Spectral Numerical Exterior Calculus Methods for Differential Equations on Radial Manifolds.
J. Sci. Comput., 2018

Hydrodynamic flows on curved surfaces: Spectral numerical methods for radial manifold shapes.
J. Comput. Phys., 2018

Importance of the Mathematical Foundations of Machine Learning Methods for Scientific and Engineering Applications.
CoRR, 2018

2016
Fluctuating Hydrodynamics Methods for Dynamic Coarse-Grained Implicit-Solvent Simulations in LAMMPS.
SIAM J. Sci. Comput., 2016

2015
Simulation of Osmotic Swelling by the Stochastic Immersed Boundary Method.
SIAM J. Sci. Comput., 2015

Stochastic Reductions for Inertial Fluid-Structure Interactions Subject to Thermal Fluctuations.
SIAM J. Appl. Math., 2015

2014
Spatially adaptive stochastic methods for fluid-structure interactions subject to thermal fluctuations in domains with complex geometries.
J. Comput. Phys., 2014

A First-Passage Kinetic Monte Carlo method for reaction-drift-diffusion processes.
J. Comput. Phys., 2014

2013
Hybrid continuum-particle method for fluctuating lipid bilayer membranes with diffusing protein inclusions.
J. Comput. Phys., 2013

2011
Stochastic Eulerian Lagrangian methods for fluid-structure interactions with thermal fluctuations.
J. Comput. Phys., 2011

2010
Spatially adaptive stochastic numerical methods for intrinsic fluctuations in reaction-diffusion systems.
J. Comput. Phys., 2010

2008
Error analysis of a stochastic immersed boundary method incorporating thermal fluctuations.
Math. Comput. Simul., 2008

2007
A stochastic immersed boundary method for fluid-structure dynamics at microscopic length scales.
J. Comput. Phys., 2007


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