5 papers
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…
Statistical finite elements for sequential data synthesis in solid dynamics
Igor Kavrakov, Yaswanth Sai Jetti, Ahmet Oguzhan Yuksel +1
We present an approach for synthesising observational data with elastodynamic finite element models by extending the statistical finite element method (statFEM) framework. The prop…
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…
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…
Data-driven Aeroelastic Analyses of Structures in Turbulent Wind Conditions using Enhanced Gaussian Processes with Aerodynamic Priors
Igor Kavrakov, Guido Morgenthal, Allan McRobie
Recent advancements in data-driven aeroelasticity have been driven by the wealth of data available in the wind engineering practice, especially in modeling aerodynamic forces. Desp…