3 papers
cs.CE2026
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…
cs.LG2026
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…
cs.CE2025
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…