High-Fidelity Digital Twin Data Models by Randomized Dynamic Mode Decomposition and Deep Learning
High-Fidelity Digital Twin Data Models by Randomized Dynamic Mode Decomposition and Deep Learning with Applications in Fluid Dynamics
This research presents a new method for creating efficient digital twin data models by combining randomized dynamic mode decomposition and deep learning, which could improve accuracy and reduce computational costs in fluid dynamics simulations.
This paper introduces a new framework for creating efficient digital twin data models by combining randomized dynamic mode decomposition and deep learning.
It defines digital twin data models (DTMs) as reduced complexity models that mirror original process behavior.
The framework is tested on three shock wave phenomena with increasing complexity, showing outputs consistent with original data while reducing complexity.
The author assesses performance in terms of numerical accuracy and computational efficiency.
High-Fidelity Digital Twin Data Models by Randomized Dynamic Mode Decomposition and Deep Learning with Applications in Fluid Dynamics
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.
This report is categorized under AITOP's "Papers" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include digital twins, deep learning.
Source attribution: curated from arXiv cs.LG (@Diana A. Bistrian).
