43 citations · 43 across the 4 of their papers we have counts for
5 papers
Reduced-Order Physics-Informed Neural Network with Adaptive Basis Refinement for Structural Identification
Rui Zhang, Konstantinos Vlachas, Eleni Chatzi
Physics-informed neural networks (PINNs) provide a flexible framework for solving forward and inverse problems. However, their direct application to structural dynamics remains lim…
Beyond Static Models: Hypernetworks for Adaptive and Generalizable Forecasting in Complex Parametric Dynamical Systems
Pantelis R. Vlachas, Konstantinos Vlachas, Eleni Chatzi
Dynamical systems play a key role in modeling, forecasting, and decision-making across a wide range of scientific domains. However, variations in system parameters, also referred t…
A Reduced Order Model conditioned on monitoring features for estimation and uncertainty quantification in engineered systems
Konstantinos Vlachas, Thomas Simpson, Anthony Garland +3
Reduced Order Models (ROMs) form essential tools across engineering domains by virtue of their function as surrogates for computationally intensive digital twinning simulators. Alt…
VpROM: A novel Variational AutoEncoder-boosted Reduced Order Model for the treatment of parametric dependencies in nonlinear systems
Thomas Simpson, Konstantinos Vlachas, Anthony Garland +2
Reduced Order Models (ROMs) are of considerable importance in many areas of engineering in which computational time presents difficulties. Established approaches employ projection-…
A local basis approximation approach for nonlinear parametric model order reduction
Konstantinos Vlachas, Konstantinos Tatsis, Konstantinos Agathos +2
The efficient condition assessment of engineered systems requires the coupling of high fidelity models with data extracted from the state of the system `as-is'. In enabling this ta…