Design to Acoustics mittels Deep Learning

• Design assistants are desirable in many areas of engineering
• Large vibroacoustic models (e.g. aircraft):
  • Numerical methods are the standard approach (e.g. finite element models)
  • Extensive parameter samplings are computationally expensive and hardly possible to consider during early design stages
• Deep Learning:
  • Increasingly used in scientific and engineering applications
  • Recent success in generative language and image modelling

• Neural networks can learn to represent vibroacoustic problems (Replacement of classic surrogates)
• Neural network architectures are crucial for generalization to new scenes
• Generative modeling is transferable to acoustic design spaces
• Generative Adversarial Networks (GANs) enable fast one-shot proposals

• Design of vibroacoustic systems by generative neural networks
• Transfer of insights and methods to other engineering disciplines

1.Representation of vibroacoustic scenes

• Fit a neural network to numerical solutions
  of vibroacoustic problems

2.Generalization to novel vibroacoustic scenes

• Apply neural networks on unseen vibroacoustic problems
  (e.g. new frequencies, new mechanical input parameters)

3.Generation of designs (e.g. geometries, material properties/distributions)

• Direct, gradient-based design space optimization
• GAN-based one-shot proposal generation