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Paul J. Atzberger

As seen in: arXiv, PLOS, Physical Review B
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Articles by Paul J. Atzberger

Protein drift-diffusion in membranes with non-equilibrium fluctuations arising from gradients in concentration or temperature

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By Paul J. Atzberger, Dev Jasuja
| PLOS
Citation: Jasuja D, Atzberger PJ (2025) Protein drift-diffusion in membranes with non-equilibrium fluctuations arising from gradients in concentration or temperature. PLoS Comput Biol 21(11): e1013678. https://doi.org/10.1371/journal.pcbi.1013678 Editor: Mark Alber, University of California Riverside, UNITED STATES OF AMERICA Received: September 29, 2024; Accepted: October 29, 2025; Published: November 21, 2025 Copyright: © 2025 Jasuja, Atzberger.
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[2405.14270] Sparse $L^1$-Autoencoders for Scientific Data Compression

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By Jack Michael, Paul J. Atzberger, Rick Archibald, Jack Michael Solomon
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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[2404.10843] Geometric Neural Operators (GNPs) for Data-Driven Deep Learning of Non-Euclidean Operators

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By Paul J. Atzberger
| arXiv
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Protein drift-diffusion dynamics and phase separation in curved cell membranes and dendritic spines: Hybrid discrete-continuum methods

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By Patrick Tran, Thomas A. Blanpied, Paul J. Atzberger
| Physical Review B
Abstract We develop methods for investigating protein drift-diffusion dynamics in heterogeneous cell membranes and the roles played by geometry, diffusion, chemical kinetics, and phase separation. Our hybrid stochastic numerical methods combine discrete particle descriptions with continuum-level models for tracking the individual protein drift-diffusion dynamics when coupled to continuum fields.
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GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions

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By Paul J. Atzberger
| arXiv
[Submitted on 10 Jun 2022 ( v1 ), last revised 26 Mar 2025 (this version, v4)] Title:GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions View a PDF of the paper titled GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions, by Ryan Lopez and Paul J.
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