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math.NA2025
Reduced Order Data-driven Twin Models for Nonlinear PDEs by Randomized Koopman Orthogonal Decomposition and Explainable Deep Learning
D. A. Bistrian
This study introduces a data-driven twin modeling framework based on modern Koopman operator theory, offering a significant advancement over classical modal decomposition by accura…
math.NA2024
Mathematical Considerations on Randomized Orthgonal Decomposition Method for Developing Twin Data Models
Diana A. Bistrian
This paper introduces the approach of Randomized Orthogonal Decomposition (ROD) for producing twin data models in order to overcome the drawbacks of existing reduced order modellin…
math.NA2024
Reduced-order modelling based on Koopman operator theory
Diana A. Bistrian, Gabriel Dimitriu, Ionel M. Navon
The present study focuses on a subject of significant interest in fluid dynamics: the identification of a model with decreased computational complexity from numerical code output u…