arXiv subjects

Richard Pausch

Publications and source records attributed to Richard Pausch.

13 recordsLinked to original sources

arXiv2025

Radiative Signatures of Magnetic Reconnection: An Approach to Remote Probing of Reconnection Dynamics

Magnetic reconnection drives a wide range of astrophysical phenomena, including geomagnetic storms, solar flares, and activity in blazars. However, direct measurement of key reconnection observables remains challenging due to the remote and extreme nature of these environments. While high-energy particle showers observed on Earth are often attributed to reconnection, the underlying mechanisms are not fully understood, and clear diagnostic signatures are lacking. We present a theoretical, data-driven approach for identifying reconnection radiation signatures and enabling remote diagnostics of reconnection in astrophysical settings through radiation spectra. Using particle-in-cell (PIC) simulations of magnetic reconnection, we generate radiation spectra and establish connections between spectral features and the underlying reconnection dynamics. We develop a method to estimate the ratio of the reconnection electric field to the plasmoid magnetic field from spectral data. Analytic calculations show that other parameters can be extracted in the ultra-relativistic reconnection regime, such as the magnetic field or the current sheet width.

astro-ph.HE↗

arXiv2025

The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations

Increasing HPC cluster sizes and large-scale simulations that produce petabytes of data per run, create massive IO and storage challenges for analysis. Deep learning-based techniques, in particular, make use of these amounts of domain data to extract patterns that help build scientific understanding. Here, we demonstrate a streaming workflow in which simulation data is streamed directly to a machine-learning (ML) framework, circumventing the file system bottleneck. Data is transformed in transit, asynchronously to the simulation and the training of the model. With the presented workflow, data operations can be performed in common and easy-to-use programming languages, freeing the application user from adapting the application output routines. As a proof-of-concept we consider a GPU accelerated particle-in-cell (PIConGPU) simulation of the Kelvin- Helmholtz instability (KHI). We employ experience replay to avoid catastrophic forgetting in learning from this non-steady process in a continual manner. We detail challenges addressed while porting and scaling to Frontier exascale system.

physics.comp-ph↗

arXiv2024

Streaming Data in HPC Workflows Using ADIOS

The "IO Wall" problem, in which the gap between computation rate and data access rate grows continuously, poses significant problems to scientific workflows which have traditionally relied upon using the filesystem for intermediate storage between workflow stages. One way to avoid this problem in scientific workflows is to stream data directly from producers to consumers and avoiding storage entirely. However, the manner in which this is accomplished is key to both performance and usability. This paper presents the Sustainable Staging Transport, an approach which allows direct streaming between traditional file writers and readers with few application changes. SST is an ADIOS "engine", accessible via standard ADIOS APIs, and because ADIOS allows engines to be chosen at run-time, many existing file-oriented ADIOS workflows can utilize SST for direct application-to-application communication without any source code changes. This paper describes the design of SST and presents performance results from various applications that use SST, for feeding model training with simulation data with substantially higher bandwidth than the theoretical limits of Frontier's file system, for strong coupling of separately developed applications for multiphysics multiscale simulation, or for in situ analysis and visualization of data to complete all data processing shortly after the simulation finishes.

cs.PF↗

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↗

arXiv2023

Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows

Understanding and control of Laser-driven Free Electron Lasers remain to be difficult problems that require highly intensive experimental and theoretical research. The gap between simulated and experimentally collected data might complicate studies and interpretation of obtained results. In this work we developed a deep learning based surrogate that could help to fill in this gap. We introduce a surrogate model based on normalising flows for conditional phase-space representation of electron clouds in a FEL beamline. Achieved results let us discuss further benefits and limitations in exploitability of the models to gain deeper understanding of fundamental processes within a beamline.

physics.acc-ph↗

arXiv2022

Continual learning autoencoder training for a particle-in-cell simulation via streaming

The upcoming exascale era will provide a new generation of physics simulations. These simulations will have a high spatiotemporal resolution, which will impact the training of machine learning models since storing a high amount of simulation data on disk is nearly impossible. Therefore, we need to rethink the training of machine learning models for simulations for the upcoming exascale era. This work presents an approach that trains a neural network concurrently to a running simulation without storing data on a disk. The training pipeline accesses the training data by in-memory streaming. Furthermore, we apply methods from the domain of continual learning to enhance the generalization of the model. We tested our pipeline on the training of a 3d autoencoder trained concurrently to laser wakefield acceleration particle-in-cell simulation. Furthermore, we experimented with various continual learning methods and their effect on the generalization.

cs.LG↗

arXiv2022

Challenges Porting a C++ Template-Metaprogramming Abstraction Layer to Directive-based Offloading

HPC systems employ a growing variety of compute accelerators with different architectures and from different vendors. Large scientific applications are required to run efficiently across these systems but need to retain a single code-base in order to not stifle development. Directive-based offloading programming models set out to provide the required portability, but, to existing codes, they themselves represent yet another API to port to. Here, we present our approach of porting the GPU-accelerated particle-in-cell code PIConGPU to OpenACC and OpenMP target by adding two new backends to its existing C++-template metaprogramming-based offloading abstraction layer alpaka and avoiding other modifications to the application code. We introduce our approach in the face of conflicts between requirements and available features in the standards as well as practical hurdles posed by immature compiler support.

cs.PL↗

arXiv2021

Data-Driven Shadowgraph Simulation of a 3D Object

In this work we propose a deep neural network based surrogate model for a plasma shadowgraph - a technique for visualization of perturbations in a transparent medium. We are substituting the numerical code by a computationally cheaper projection based surrogate model that is able to approximate the electric fields at a given time without computing all preceding electric fields as required by numerical methods. This means that the projection based surrogate model allows to recover the solution of the governing 3D partial differential equation, 3D wave equation, at any point of a given compute domain and configuration without the need to run a full simulation. This model has shown a good quality of reconstruction in a problem of interpolation of data within a narrow range of simulation parameters and can be used for input data of large size.

cs.LG↗

arXiv2021

Invertible Surrogate Models: Joint surrogate modelling and reconstruction of Laser-Wakefield Acceleration by invertible neural networks

Invertible neural networks are a recent technique in machine learning promising neural network architectures that can be run in forward and reverse mode. In this paper, we will be introducing invertible surrogate models that approximate complex forward simulation of the physics involved in laser plasma accelerators: iLWFA. The bijective design of the surrogate model also provides all means for reconstruction of experimentally acquired diagnostics. The quality of our invertible laser wakefield acceleration network will be verified on a large set of numerical LWFA simulations.

physics.plasm-ph↗

arXiv2020

Large-scale Neural Solvers for Partial Differential Equations

Solving partial differential equations (PDE) is an indispensable part of many branches of science as many processes can be modelled in terms of PDEs. However, recent numerical solvers require manual discretization of the underlying equation as well as sophisticated, tailored code for distributed computing. Scanning the parameters of the underlying model significantly increases the runtime as the simulations have to be cold-started for each parameter configuration. Machine Learning based surrogate models denote promising ways for learning complex relationship among input, parameter and solution. However, recent generative neural networks require lots of training data, i.e. full simulation runs making them costly. In contrast, we examine the applicability of continuous, mesh-free neural solvers for partial differential equations, physics-informed neural networks (PINNs) solely requiring initial/boundary values and validation points for training but no simulation data. The induced curse of dimensionality is approached by learning a domain decomposition that steers the number of neurons per unit volume and significantly improves runtime. Distributed training on large-scale cluster systems also promises great utilization of large quantities of GPUs which we assess by a comprehensive evaluation study. Finally, we discuss the accuracy of GatedPINN with respect to analytical solutions -- as well as state-of-the-art numerical solvers, such as spectral solvers.

cs.LG↗

arXiv2020

Laser-plasma proton acceleration with a combined gas-foil target

Laser-plasma proton acceleration was investigated in the Target Normal Sheath Acceleration (TNSA) regime using a novel gas-foil target. The target is designed for reaching higher laser intensity at the foil plane owing to relativistic self-focusing and self compression of the pulse in the gas layer. Numerical 3D particle-in-cell (PIC) simulations were used to study pulse propagation in the gas, showing a nearly seven-fold increase in peak intensity. In the experiment, maximum proton energies showed high dependence on the energy transmission of the laser through the gas and a lesser dependence on the size and shape of the pulse. At high gas densities, laser energy depletion and pulse distortion suppressed proton energies. At low densities, self-focusing was observed and comparable or higher proton energies were measured with the gas.

physics.plasm-ph↗

arXiv2018

Quantitatively consistent computation of coherent and incoherent radiation in particle-in-cell codes - a general form factor formalism for macro-particles

Quantitative predictions from synthetic radiation diagnostics often have to consider all accelerated particles. For particle-in-cell (PIC) codes, this not only means including all macro-particles but also taking into account the discrete electron distribution associated with them. This paper presents a general form factor formalism that allows to determine the radiation from this discrete electron distribution in order to compute the coherent and incoherent radiation self-consistently. Furthermore, we discuss a memory-efficient implementation that allows PIC simulations with billions of macro-particles. The impact on the radiation spectra is demonstrated on a large scale LWFA simulation.

physics.comp-ph↗

arXiv2014

Visualizing the Radiation of the Kelvin-Helmholtz Instability

Emerging new technologies in plasma simulations allow tracking billions of particles while computing their radiative spectra. We present a visualization of the relativistic Kelvin-Helmholtz Instability from a simulation performed with the fully relativistic particle-in-cell code PIConGPU powered by 18,000 GPUs on the USA's fastest supercomputer Titan [1].

physics.plasm-ph↗