Shock Propagation

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1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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  1. High-Fidelity Digital Twin Data Models by Randomized Dynamic Mode Decomposition and Deep Learning with Applications in Fluid Dynamics

    Sep 15, 2026Diana A. BistrianDigital TwinsDynamic Mode Decomposition

    University Politehnica Timisoara, Department of Electrical Engineering and Industrial Informatics, Romania

  2. Amortized Interventional Forecasting for Multivariate CIR Processes

    Aug 4, 2026Andreas Sauter, Sumit Sourabh, Drona Kandhai +1Time-To-Event DataData-Driven Forecasting

    Vrije Universiteit Amsterdam · ING · University of Amsterdam / ING +1

  3. Comparison of a Parametric Physics-Informed Neural Network and a Tensorial Reduced-Order Model for the Shallow-Water Dam-Break Problem

    Jul 29, 2026Anton Myshak, Md Rezwan Bin Mizan, Ilya TimofeyevParametric Physics-Informed Neural NetworkShock Propagation

    Dept. of Mathematics, University of Houston, Houston, TX 77204

  4. SPARC-Net: A Spectral, Causality-Aware, and Hard-Constrained Physics-Informed Architecture for Stiff and Shock-Dominated Partial Differential Equations

    Jul 13, 2026Divyavardhan Singh, Dimple Sonone, Hammad Mohammad +1Parametric Physics-Informed Neural NetworkPartial Differential Equations

    Dept. of Electronics Engg. SVNIT Surat, India

  5. A Link between Shock-wave Theory and Symmetry-reduced Stochastic Gradient Descent for Artificial Neural Networks

    Jun 16, 2026Taiki MiyagawaStochastic Gradient DescentNeural Network

    NEC Corporation

  6. Feature-preserving Latent-EnKF for Data Assimilation of Flows with Shocks

    Jun 10, 2026Hemanth Chandravamsi, Hangchuan Hu, Ponkrshnan Thiagarajan +1Data AssimilationShock Propagation

    Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA

  7. How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit

    Jun 10, 2026Ana Larrañaga, Urban Fasel, Steven L. BruntonSystem IdentificationActive Learning

    Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, United States · NSF AI Institute in Dynamic Systems, University of Washington, Seattle, WA 98195, United States · Department of Aeronautics, Imperial College London, SW7 2AZ, United Kingdom

  8. Overcoming "Physics Shock" in Earth Observation A Heteroscedastic Uncertainty Framework for PINN-based Flood Inference

    May 22, 2026Tewodros Syum Gebre, Jagrati Talreja, Matilda Anokye +1Parametric Physics-Informed Neural NetworkFlood Detection

    Built Environment Department, College of Science and Technology, North Carolina A&T State University, Greensboro, NC 27405 USA · United Nations University Institute for Water, Environment and Health, Richmond Hill, ON, Canada

  9. Open-source LLMs administer maximum electric shocks in a Milgram-like obedience experiment

    May 20, 2026Roland Pihlakas, Jan Llenzl DagohoyConformityAttractors

    for the Three Laws collaboration · Independent researcher · Three Laws research collaboration +2

  10. The impact of observation density on Bayesian inversion of latent dynamics in shock-dominated flows

    May 18, 2026Bipin Tiwari, Muhammad Abid, Omer SanBayesian Inverse ProblemsShock Propagation

    Department of Mechanical and Aerospace Engineering, University of Tennessee, Knoxville Knoxville, TN 37996, USA.

  11. Crash Assessment via Mesh-Based Graph Neural Networks and Physics-Aware Attention

    May 12, 2026Gabriel Curtosi, Carlos Manuel Ruiz Ruiz, Fabiola Cavaliere +1CrashesAccident Source Term Evaluation Code

    SEAT S.A., Barcelona, Spain · IDIADA Automotive Technology S.A., Santa Oliva, Spain

  12. Local Truncation Error-Guided Neural ODEs for Large Scale Traffic Forecasting

    May 5, 2026Xiao Zhang, Yafei Li, Ruixiang Wang +3Neural Ordinary Differential EquationsOrdinary Differential Equations

    Zhengzhou University · Northwestern Polytechnical University

  13. The High Explosives and Affected Targets (HEAT) Dataset

    Apr 20, 2026Bryan Kaiser, Kyle Hickmann, Sharmistha Chakrabarti +6Shock PropagationSurrogate Models

    Los Alamos National Laboratory

  14. Inverse Reconstruction of Shock Time Series from Shock Response Spectrum Curves using Machine Learning

    Mar 3, 2026Adam Watts, Andrew Jeon, Destry Newton +1Signal ReconstructionShock Propagation

    Los Alamos National Laboratory · University of Rochester