To overcome the limitations associated with purely physics-based and data-driven modeling
methods, hybrid, physics-based data-driven models have been developed, with improved …

Abstract We present Lift & Learn, a physics-informed method for learning low-dimensional
models for large-scale dynamical systems. The method exploits knowledge of a system's …

This paper presents a novel framework that enables the generation of unbiased estimates
for test loss using fewer labeled samples, effectively evaluating the predictive performance …

We present a new scientific machine learning method that learns from data a
computationally inexpensive surrogate model for predicting the evolution of a system …

Prognostics and health management (PHM) are emerging approaches to product life cycle
that will maintain system safety and improve reliability, while reducing operating and …

This work develops an efficient real-time inverse formulation for inferring the aerodynamic
surface pressures on a hypersonic vehicle from sparse measurements of the structural …

Prognostics and Health Management are emerging approaches to product life cycle that will
improve the system safety and reliability while reducing operating and maintenance costs …

This study investigates the application of machine learning models to predict time-evolving
stress fields in complex three-dimensional structures trained with full-scale finite element …

In recent years aeronautical systems are becoming increasingly complex, as they are often
required to perform various functions. New intelligent systems are required capable of self …

The real-time identification of inflow aerodynamic parameters such as the flow separation
situation, angle of attack, and inflow velocity is challenging. In this paper, a new data-driven …