Design to Acoustics Through 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
• Application of generative models for engineering problems in acoustics
  • Desired scene (sound pressure levels):
    • (Input)  -> Generative deep learning model (acoustic design assistant)
    • (One-shot & acoustic design)  -> Preliminary aircraft design (dimensions, materials, sitting plan, etc.)

• Generate design proposals for desirable acoustics.
• Gathered insights and developed methods can be applicable for other engineering disciplines: FEM -> Representation -> Design proposals.

• Useful network topologies in deep learning
  • Bestle/Eberhard
• Transient and non-linear systems in vibroacoustics
  • Stender
• Physics-informed NNs for vibroacoustics
  • Peitz/Sextro
• Meshing
  • Riedelbauch

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