GAMM2025

Session

S23: More design assistents

S23

8 Apr 2025, 16:30

Lecture and Conference Centre

Conveners

S23: More design assistents: S23.01

• Mario Rosenfelder
• Kristin de Payrebrune

439. Multi-objective Design Optimization for Axial Turbine via Deep Learning-Assisted Latent Space Exploration

Rohit Raj, Tobias Rentschler

08/04/2025, 16:30

Multi-objective optimization for a hydraulic turbine blade is a significant challenge due to the high computational cost of performing the number of computational fluid dynamics (CFD) simulations. It becomes more challenging while dealing with a high number of parameters. This research addresses this issue by proposing an innovative approach that leverages Autoencoder techniques to reduce the...

440. Physics-constrained frequency response prediction of structural dynamic systems via deep learning

Christian Libner

08/04/2025, 16:50

Understanding structural vibrations is critical for the design of mechanical structures such as airplanes or cars. In the context of vibroacoustic design the dynamical response of the system in the frequency domain is central. Such frequency response functions are usually computed using discretization techniques such as the finite element method. To this end, potentially large linear systems...

441. Learning Differential Equations from Numerically Integrated Artificial Neural Networks

Timo Bielitz

08/04/2025, 17:10

Determination of future states based on the actual one is a key task in many areas of dynamic system analysis like forecasting, stability analysis or model predictive control. For this purpose, classically a mathematical model needs to be set up from physical principles. The resulting equations of motion are then usually transformed into a system of 1st-order ordinary differential equations...