Cooperative neural networks for inverse design: Integrating denoising autoencoder and surrogate model for partial design variable imputation

Data-driven inverse design is an engineering approach where target performance criteria are specified upfront, leading to the derivation of design solutions that meet these criteria. While recent research focuses on generating complete design solutions using generative models, these approaches struggle with partial design variables and constraints that predetermine certain variables. Additionally, generative models are data-intensive and prone to overfitting with limited datasets. To address these limitations, this paper proposes a Cooperative Neural Network architecture comprising two key components: the Imputation Model and the Surrogate Model. These components collaborate to optimize design solutions while adhering to predefined performance criteria. The framework’s effectiveness is demonstrated through a case study on Glass Run Channel (GRC) designs from a Korean automotive manufacturer. Results show the architecture proficiently imputes undetermined variables and ensures the designs meet desired performance metrics, achieving Mean Squared Error (MSE) reductions of up to 98 % and R-squared values of 0.997–0.999 in initial tests. It remains robust in diverse scenarios, achieving up to 95.65 % MSE reduction and R-squared values of 0.995–0.999 for cases with the most undetermined variables, and up to 94.68 % MSE reduction with R-squared values of 0.983–0.995 for the smallest training datasets. This framework reduces design cycle times and enhances engineering design efficiency, offering a robust solution to limitations in traditional methods reliant on physical prototyping and iterative testing.