MS003B Physics-Informed Machine Learning for Surrogate Models in Continuum Mechanics II
Main Organizer:
Ms.
Veronika Travnikova
(
RWTH Aachen University
, Germany
)
Chaired by:
Ms. Veronika Travnikova (RWTH Aachen University , Germany) , Dr. Matthias Möller (Delft University of Technology , Netherlands)
Ms. Veronika Travnikova (RWTH Aachen University , Germany) , Dr. Matthias Möller (Delft University of Technology , Netherlands)
Scheduled presentations:
-
A machine learning-based surrogate model for an efficient homogenization of open-porous materials
-
Student
On The Combination Of Physically-Guided Neural Networks With Internal Variables And Differential Operators For The Discovery Of Nonlinear, Anisotropic And Heterogeneous Features In Material Sciences
-
Identification of Viscoelastic Material Properties Using a Physics-Informed Neural Network Framework with Non-Uniform Complex Modulus
-
Physics-informed Deep Neural Networks towards Finite Strain Homogenization of Unidirectional Soft Composites
-
PINNS applied to vibrations of overhead line cables
