Design of Unmanned Air Vehicles Using Transformer Surrogate Models
- URL: http://arxiv.org/abs/2211.08138v1
- Date: Fri, 11 Nov 2022 21:22:21 GMT
- Title: Design of Unmanned Air Vehicles Using Transformer Surrogate Models
- Authors: Adam D. Cobb, Anirban Roy, Daniel Elenius, Susmit Jha
- Abstract summary: We develop an AI Designer that synthesizes novel unmanned aerial vehicles (UAVs) designs.
Our approach uses a deep transformer model with a novel domain-specific encoding such that we can evaluate the performance of new proposed designs without running expensive flight dynamics models and CAD tools.
- Score: 8.914156789222266
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Computer-aided design (CAD) is a promising new area for the application of
artificial intelligence (AI) and machine learning (ML). The current practice of
design of cyber-physical systems uses the digital twin methodology, wherein the
actual physical design is preceded by building detailed models that can be
evaluated by physics simulation models. These physics models are often slow and
the manual design process often relies on exploring near-by variations of
existing designs. AI holds the promise of breaking these design silos and
increasing the diversity and performance of designs by accelerating the
exploration of the design space. In this paper, we focus on the design of
electrical unmanned aerial vehicles (UAVs). The high-density batteries and
purely electrical propulsion systems have disrupted the space of UAV design,
making this domain an ideal target for AI-based design. In this paper, we
develop an AI Designer that synthesizes novel UAV designs. Our approach uses a
deep transformer model with a novel domain-specific encoding such that we can
evaluate the performance of new proposed designs without running expensive
flight dynamics models and CAD tools. We demonstrate that our approach
significantly reduces the overall compute requirements for the design process
and accelerates the design space exploration. Finally, we identify future
research directions to achieve full-scale deployment of AI-assisted CAD for
UAVs.
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