Multi-Material Shock Physics

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Period ending 2026-09-21

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High-Fidelity Digital Twin Data Models by Randomized Dynamic Mode Decomposition and Deep Learning with Applications in Fluid Dynamics
Sep 15, 2026Diana A. BistrianCognitive Digital Twins Fluid Dynamics
Amortized Interventional Forecasting for Multivariate CIR Processes
Aug 4, 2026Andreas Sauter, Sumit Sourabh, Drona Kandhai +1Time-To-Event Data Gaussian Copula
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 TimofeyevConventional Physics-Informed Neural Networks Multi-Material Shock Physics
Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows
Jul 24, 2026Harish Ramachandran, Björn Kimpel, Thomas Paula +3Multi-Material Shock Physics Multiscale Dynamics
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 +1Partial Differential Equation Benchmarks Partial Differential Equations
A Deep Learning-based surrogate model for Severe Accidents in nuclear reactors using ASTEC
Jul 5, 2026Alessandro Longhi, Danny Lathouwers, Zoltán PerkóSurrogate Models Nuclear Fusion
A Link between Shock-wave Theory and Symmetry-reduced Stochastic Gradient Descent for Artificial Neural Networks
Jun 16, 2026Taiki MiyagawaTwo-Layer Neural Networks Stochastic Gradient Descent
Feature-preserving Latent-EnKF for Data Assimilation of Flows with Shocks
Jun 10, 2026Hemanth Chandravamsi, Hangchuan Hu, Ponkrshnan Thiagarajan +1Data Assimilation Multi-Material Shock Physics
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 Identification Active Learning
High-Fidelity Industrial Crash Dynamics Prediction via Geometry-Aware Operator Learning with Memory-Efficient Low-Rank Attention
May 26, 2026Deepak Akhare, Mohammad Amin Nabian, Corey Adams +2Accidents Multi-Material Shock Physics
Overcoming "Physics Shock" in Earth Observation A Heteroscedastic Uncertainty Framework for PINN-based Flood Inference
May 22, 2026Tewodros Syum Gebre, Jagrati Talreja, Matilda Anokye +1Flood Detection Conventional Physics-Informed Neural Networks
Open-source LLMs administer maximum electric shocks in a Milgram-like obedience experiment
May 20, 2026Roland Pihlakas, Jan Llenzl DagohoyConformity Large Language
The impact of observation density on Bayesian inversion of latent dynamics in shock-dominated flows
May 18, 2026Bipin Tiwari, Muhammad Abid, Omer SanMulti-Material Shock Physics Ill-Posed Inverse Problem
Crash Assessment via Mesh-Based Graph Neural Networks and Physics-Aware Attention
May 12, 2026Gabriel Curtosi, Carlos Manuel Ruiz Ruiz, Fabiola Cavaliere +1Crashes Accidents
Local Truncation Error-Guided Neural ODEs for Large Scale Traffic Forecasting
May 5, 2026Xiao Zhang, Yafei Li, Ruixiang Wang +3Neural Ordinary Differential Equations Ordinary Differential Equations
CRADIPOR: Crash Dispersion Predictor
Apr 30, 2026Edgar Chaillou, Sebastian Rodriguez, Yves Tourbier +1Crashes Accidents
The High Explosives and Affected Targets (HEAT) Dataset
Apr 20, 2026Bryan Kaiser, Kyle Hickmann, Sharmistha Chakrabarti +6Multi-Material Shock Physics Inertial Confinement Fusion
Inverse Reconstruction of Shock Time Series from Shock Response Spectrum Curves using Machine Learning
Mar 3, 2026Adam Watts, Andrew Jeon, Destry Newton +1Spectral Reconstruction Multi-Material Shock Physics

Sep 15, 2026 · cs.LGJ/K move · Enter open · S save

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

Diana A. Bistrian

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

The purpose of this paper is the identification of high-fidelity digital twin data models from numerical code outputs by non-intrusive techniques (i.e., not requiring Galerkin projection of the governing equations onto the reduced modes basis). In this paper the author defines the concept of the digital twin data model (DTM) as a model of reduced complexity that has the main feature of mirroring the original process behavior. The significant advantage of a DTM is to reproduce the dynamics with high accuracy and reduced costs in CPU time and hardware for settings difficult to explore because of the complexity of the dynamics over time. This paper introduces a new framework for creating efficient digital twin data models by combining two state-of-the-art tools: randomized dynamic mode decomposition and deep learning artificial intelligence. It is shown that the outputs are consistent with the original source data with the advantage of reduced complexity. The DTMs are investigated in the numerical simulation of three shock wave phenomena with increasing complexity. The author performs a thorough assessment of the performance of the new digital twin data models in terms of numerical accuracy and computational efficiency.

Cognitive Digital Twins Fluid Dynamics Multi-Material Shock Physics Dynamics Models High-Fidelity Deep Learning

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