23 citations · 82 across the 11 of their papers we have counts for
5 papers · 1 filter
Aerodynamic force reconstruction using physics-informed Gaussian processes
Gledson Rodrigo Tondo, Igor Kavrakov, Guido Morgenthal
Accurate modeling of aerodynamic loads is essential for understanding and predicting the responses of complex structural systems. However, these models often rely on simplification…
Efficient dynamic modal load reconstruction using physics-informed Gaussian processes based on frequency-sparse Fourier basis functions
Gledson Rodrigo Tondo, Igor Kavrakov, Guido Morgenthal
Knowledge of the force time history of a structure is essential to assess its behaviour, ensure safety and maintain reliability. However, direct measurement of external forces is o…
Stochastic Inference of Plate Bending from Heterogeneous Data: Physics-informed Gaussian Processes via Kirchhoff-Love Theory
Igor Kavrakov, Gledson Rodrigo Tondo, Guido Morgenthal
Advancements in machine learning and an abundance of structural monitoring data have inspired the integration of mechanical models with probabilistic models to identify a structure…
Stochastic stiffness identification and response estimation of Timoshenko beams via physics-informed Gaussian processes
Gledson Rodrigo Tondo, Sebastian Rau, Igor Kavrakov +1
Machine learning models trained with structural health monitoring data have become a powerful tool for system identification. This paper presents a physics-informed Gaussian proces…
A physics-informed machine learning model for reconstruction of dynamic loads
Gledson Rodrigo Tondo, Igor Kavrakov, Guido Morgenthal
Long-span bridges are subjected to a multitude of dynamic excitations during their lifespan. To account for their effects on the structural system, several load models are used dur…