Design to Acoustics through Deep Learning

通过深度学习进行声学设计

• 批准号: 501927736
• 负责人: Professorin Dr.-Ing. Sabine C. Langer
• 金额: --
• 依托单位: Institut für Informatik (IfI)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/501927736?language=en
• 关键词: Design; Acoustics; through; Deep; Learning

The applicants Langer and Lüddecke propose - Design to Acoustics Through Deep Learning - in order to drive a paradigm shift in acoustic design. We plan to develop intelligent design assistants that support the engineer by generating design proposals for predefined, desired acoustic properties. A consideration of acoustics from the very beginning design stages is forced, in which the design can still be significantly influenced. Reasoned by negative environmental impacts of high noise levels (health, discomfort), great efforts are made to reduce sound pressure levels in product and vehicle design. Unfortunately, acoustic problem often arise in late design stages, which often leads to expensive noise abatement measures. A deep consideration of acoustics in early phases is expected to unlock a great potential for low noise designs. We propose a data-driven neural network-based approach. First, we train a forward model to predict sound pressure levels obtained using finite element models of selected academic examples. Different forms of specifying this training and the model's architecture are compared. Second, we generate design proposals through two different data-driven approaches, which are novel to this field: Directly optimising in design space using backpropagation and generative adversarial networks (GANs). The former conducts optimisation based on analytical gradients, which are favourably accessible through neural networks. The GAN-based approach follows its successful application in image generation and shall deliver one-shot design proposals without any iteration. Both methods will leverage insights gained from studying the forward model. Finally, we employ our system in a challenging aircraft fuselage problem to demonstrate the applicability in real-world problems. This interdisciplinary proposal combines the competency of Langer in computational acoustics with the experience of Lüddecke in deep learning.

申请人Langer和Lüddecke提出-通过深度学习进行声学设计-以推动声学设计的范式转变。我们计划开发智能设计助手，通过为预定义的所需声学特性生成设计方案来支持工程师。 从一开始的设计阶段就必须考虑声学问题，这仍然会对设计产生重大影响。考虑到高噪声水平对环境的负面影响（健康、不适），在产品和车辆设计中努力降低声压级。不幸的是，声学问题往往出现在后期的设计阶段，这往往导致昂贵的降噪措施。在早期阶段对声学的深入考虑有望为低噪声设计带来巨大潜力。我们提出了一种基于数据驱动神经网络的方法。首先，我们训练一个前向模型来预测使用选定的学术实例的有限元模型获得的声压级。不同形式的指定这种培训和模型的架构进行了比较。其次，我们通过两种不同的数据驱动方法生成设计方案，这在该领域是新颖的：使用反向传播和生成对抗网络（GAN）直接优化设计空间。前者基于分析梯度进行优化，这是有利的，通过神经网络访问。基于GAN的方法遵循其在图像生成中的成功应用，并应提供一次性设计方案，无需任何迭代。这两种方法都将利用从研究正向模型中获得的见解。最后，我们采用我们的系统在一个具有挑战性的飞机机身问题，以证明在现实世界中的问题的适用性。这个跨学科的提案结合了Langer在计算声学方面的能力和Lüddecke在深度学习方面的经验。