Paul J. Atzberger
Orcid: 0000-0001-6806-8069
According to our database1,
Paul J. Atzberger
authored at least 33 papers
between 2007 and 2025.
Collaborative distances:
Collaborative distances:
Timeline
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Online presence:
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on zbmath.org
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on orcid.org
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Bibliography
2025
Transferable Foundation Models for Geometric Tasks on Point Cloud Representations: Geometric Neural Operators.
CoRR, March, 2025
GD-VAEs: Geometric dynamic variational autoencoders for learning nonlinear dynamics and dimension reductions.
J. Comput. Phys., 2025
2024
Geometric neural operators (gnps) for data-driven deep learning in non-euclidean settings.
Mach. Learn. Sci. Technol., 2024
SDYN-GANs: Adversarial learning methods for multistep generative models for general order stochastic dynamics.
J. Comput. Phys., 2024
Geometric Neural Operators (GNPs) for Data-Driven Deep Learning of Non-Euclidean Operators.
CoRR, 2024
2023
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
2021
Drift-Diffusion Dynamics and Phase Separation in Curved Cell Membranes and Dendritic Spines: Hybrid Discrete-Continuum Methods.
CoRR, 2021
CoRR, 2021
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
CoRR, 2020
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
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
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
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