23 citations · 82 across the 11 of their papers we have counts for
11 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…
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…
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…
Physics-informed Gaussian process model for Euler-Bernoulli beam elements
Gledson Rodrigo Tondo, Sebastian Rau, Igor Kavrakov +1
A physics-informed machine learning model, in the form of a multi-output Gaussian process, is formulated using the Euler-Bernoulli beam equation. Given appropriate datasets, the mo…
Framework for a simulation-based aerodynamic shape optimization of bridge decks for different limit state phenomena
Tajammal Abbas, Igor Kavrakov, Guido Morgenthal +1
Wind-induced response governs the design of the long-span bridges. The shape of the deck is one of the most important factors that not only affects the mechanical properties but gr…
Numerical investigation of the nonlinear interaction between the sinusoidal motion-induced and gust-induced forces acting on bridge decks
Samuel Tesfaye, Igor Kavrakov, Guido Morgenthal
With the increasing spans and complex deck shapes, aerodynamic nonlinearity becomes a crucial concern in the design of long-span bridges. This paper investigates the nonlinear inte…
Data-driven Aerodynamic Analysis of Structures using Gaussian Processes
Igor Kavrakov, Allan McRobie, Guido Morgenthal
An abundant amount of data gathered during wind tunnel testing and health monitoring of structures inspires the use of machine learning methods to replicate the wind forces. This p…