AIDSe

AIDSe, short for Artificial Intelligence for Design, Synthesis, and Evaluation, is a framework and methodology that integrates artificial intelligence into design workflows across engineering, architecture, and software domains. It aims to accelerate ideation by generating design alternatives, automating synthesis of components, and assessing performance under constraints using data-driven models and simulations. The approach emphasizes traceability, reliability, and explainability of AI-assisted decisions.

Conceptualized in the late 2010s as computing power and data availability increased, AIDSe matured through research in model-based design, digital twins, and generative modeling. It has been implemented as both process guidelines and software toolkits that connect design environments with AI modules, data pipelines, and simulation engines.

AIDSe architectures typically feature a layered structure including data management, AI reasoning and generation, evaluation and optimization, and integration with existing design tools. Key capabilities include generative design, multi-objective optimization, constraint handling, and explainable AI methods to provide rationale for design choices and enable human oversight.

Applications span automotive and aerospace components, civil and architectural projects, consumer electronics, and software architecture. While proponents cite reduced iteration cycles and expanded exploration of design spaces, critics warn of data requirements, potential bias, reproducibility concerns, and the need for rigorous validation of AI-generated designs. See also generative design, explainable AI, model-based design.