arXiv subjects

Benjamin Hernandez

Publications and source records attributed to Benjamin Hernandez.

3 recordsLinked to original sources

arXiv2026

Aerosol memory in stratocumulus clouds leads to noise-induced patterns and non-ergodic sampling

Stratocumulus cloud decks exhibit bistability between patterns of high (closed cells) and low (open cells) cloud fraction. Localized transitions between these two states (pockets of open cells) have been observed but their underlying mechanism remains unclear. We model stratocumulus and their interaction with atmospheric aerosol as a data-driven and physics-informed stochastic dynamical system with time-dependent parameters. This allows us to show that pockets of open cells result from noise-induced transitions between the stratocumulus patterns. We find comparable timescales for these transitions, mesoscale self-organization into patterns and the evolution of large-scale parameters. This lack of timescale separation corresponds to an aerosol memory in cloud evolution and means that the sampling of stratocumulus states by polar-orbiting satellites lacks the encoding of process information that would be present for an asymptotic and ergodic sampling.

physics.ao-ph↗

arXiv2023

EZ: An Efficient, Charge Conserving Current Deposition Algorithm for Electromagnetic Particle-In-Cell Simulations

We present EZ, a novel current deposition algorithm for particle-in-cell (PIC) simulations. EZ calculates the current density on the electromagnetic grid due to macro-particle motion within a time step by solving the continuity equation of electrodynamics. Being a charge conserving hybridization of Esirkepov's method and ZigZag, we refer to it as ``EZ'' as shorthand for ``Esirkepov meets ZigZag''. Simulations of a warm, relativistic plasma with PIConGPU show that EZ achieves the same level of charge conservation as the commonly used method by Esirkepov, yet reaches higher performance for macro-particle assignment-functions up to third-order. In addition to a detailed description of the functioning of EZ, reasons for the expected and observed performance increase are given, and guidelines for its implementation aiming at highest performance on GPUs are provided.

physics.comp-ph↗

arXiv2022

Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer

Deep learning has contributed to major advances in the prediction of protein structure from sequence, a fundamental problem in structural bioinformatics. With predictions now approaching the accuracy of crystallographic resolution in some cases, and with accelerators like GPUs and TPUs making inference using large models rapid, fast genome-level structure prediction becomes an obvious aim. Leadership-class computing resources can be used to perform genome-scale protein structure prediction using state-of-the-art deep learning models, providing a wealth of new data for systems biology applications. Here we describe our efforts to efficiently deploy the AlphaFold2 program, for full-proteome structure prediction, at scale on the Oak Ridge Leadership Computing Facility's resources, including the Summit supercomputer. We performed inference to produce the predicted structures for 35,634 protein sequences, corresponding to three prokaryotic proteomes and one plant proteome, using under 4,000 total Summit node hours, equivalent to using the majority of the supercomputer for one hour. We also designed an optimized structure refinement that reduced the time for the relaxation stage of the AlphaFold pipeline by over 10X for longer sequences. We demonstrate the types of analyses that can be performed on proteome-scale collections of sequences, including a search for novel quaternary structures and implications for functional annotation.

q-bio.QM↗