[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121113-en":3,"doc-seo-121113-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121113,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",6,"Technology","Machine Learning Visualization Tool for Exploring Parameterized Hydrodynamics - Abstract","The document presents an interactive machine learning tool for shock hydrodynamics research, focused on compressible fluids and nonlinear, dynamically evolving instabilities. With high performance computing enabling parameterized studies that generate O(TB) simulation state data, the tool compresses, browses, and interpolates large datasets. It supports rapid visualization of “what-if” scenarios, enables sensitivity analysis to initial-condition variations, and helps optimize complex hydrodynamic experiments.","arXiv :2406 . 15509v1 [physics .comp-ph] 20 Jun 2024  \nMACHINE LEARNING VISUALIZATION TOOL FOR EXPLORING PARAMETERIZED HYDRODYNAMICS  \nC. F. Jekel* D. M. Sterbentz T. M. Stitt P. Mocz R. N. Rieben D. A. White J. L. Belof  \nLawrence Livermore National Laboratory, PO Box 808, Livermore, CA,94551, USA†  \nJune 25, 2024  \nABSTRACT  \nWe are interested in the computational study of shock hydrodynamics, i.e. problems involving compressible solids, liquids, and gases that undergo large deformation. These problems are dynamic and nonlinear and can exhibit complex instabilities. Due to advances in high performance computing it is possible to parameterize a hydrodynamic problem and perform a computational study yielding O (TB) of simulation state data. We present an interactive machine learning tool that can be used to compress, browse, and interpolate these large simulation datasets. This tool allows computational scientists and researchers to quickly visualize “what-if” situations, perform sensitivity analyses, and optimize complex hydrodynamic experiments.  \n1 Introduction  \nFor hydrodynamics, it can be very difficult to understand the sensitivity of physical instabilities to small perturbations in initial conditions. It is possible for a human to understand the impact of one or two inputs on the temporal evolution of an instability. However, as the number of system parameters grow, so does the complexity of the system. Consider the Rayleigh-Taylor instability (RTI) and Richtmyer-Meshkov instabilities (RMI) which can have several inputs that influence the transient state. In these cases, ensembles of simulations are required to understand the sensitivity of these instabilities with respect to their initial states. Often these simulation results are computationally expensive, and it is difficult for researchers to look at every simulation result. The Cinema project [1] set out to aid researchers in understanding ensemble calculations by providing tools to quickly look through simulation results. The tools provided an intuitive interface for researchers to explore pictures of simulation results. While this tool is quite useful, the results are limited to only the performed calculations. Our work builds upon this concept of allowing researchers to quickly explore ensemble calculations, with the main advantage being the ability to quickly visualize results that were not previously calculated. This interpolation is accomplished with a machine learning (ML) model that allows a user to view the temporal evolution of instabilities by seamlessly changing initial conditions.  \nThe RTI and RMI are closely related. A RTI occurs at the interface of two fluids mixing with different densities. A RMI occurs when a shock wave amplifies perturbations at a material interface, causing large jet-like growths [2, 3, 4, 5] . The use and understanding of the transient behavior of these instabilities is important in many applications. For example, experimentally measuring RMI formations is useful for calibrating high strain rate material models [6, 7] . Additionally, in inertial confinement fusion (ICF) experiments, where lasers are used to heat and compress a fuel capsule to the point of starting a self-sustaining fusion reaction [8] . Unfortunately, RMI have been known to form within ICF capsules. The  \n∗[jekel1@llnl.gov](jekel1@llnl.gov)  \n†This manuscript has been authored by Lawrence Livermore National Security, LLC under Contract No. DE-AC52-07NA2 7344 with the US. Department of Energy. The United States Government retains, and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. LLNL-JRNL-865692  \nRMI often destroys the fuel target before fusion ignition is achieved [9] . Increasing our ability to design and cont","cbCaioizTw5va2jC","https://ap.wps.com/l/cbCaioizTw5va2jC","pdf",10723002,1,37,"English","en",105,"# Abstract\n# Introduction\n## Hydrodynamic instabilities and ensemble sensitivity\n## Interpolation and ML-based exploration of simulation datasets\n## Related ML work for parameterized fluid systems","[{\"question\":\"What problem does the proposed tool address in shock hydrodynamics research?\",\"answer\":\"It addresses the difficulty of understanding how nonlinear instabilities respond to changes in multiple initial-condition parameters when ensembles of simulations become too expensive and hard to browse exhaustively.\"},{\"question\":\"How does the tool help researchers work with very large simulation datasets?\",\"answer\":\"It compresses, browses, and interpolates O(TB) scale simulation state data, allowing scientists to quickly explore results and generate visualizations for parameter variations.\"},{\"question\":\"What new capability does the work add beyond the Cinema project?\",\"answer\":\"Unlike tools limited to already-computed simulation results, the approach uses a machine learning model to visualize temporal evolution while seamlessly changing initial conditions, enabling inference of results not previously calculated.\"}]","Machine Learning Visualization Tool for Exploring Parameterized Hydrodynamics - 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