A Unified Computational Framework for Generative Aesthetics and Inverse Design in Autonomous Artifact Design Systems

With the rapid development of artificial intelligence, a shift in the approach to artifact crafting is occurring. Instead of artisans creating artifacts, autonomous systems are beginning to create artifacts with the same intention as their human creators. One proposed method for accomplishing this goal is the development of an AI-driven computational framework that integrates the concepts of both generative aesthetics and inverse design. Each of these concepts contributes to the development of autonomous systems capable of understanding the relationship between artifacts, their components, and their intended performance, allowing for the creation of diverse and optimal artifacts. More specifically, AI models can propose a variety of different artifacts, surrogate models can evaluate each of those proposed artifacts for their potential performance, and the incorporation of inverse optimization techniques can allow for the artifacts to be continuously refined until they exhibit the optimal performance. Additionally, incorporating models with the interpretability and complex nonlinear relationship mapping capabilities of Kolmogorov-Arnold Networks (KANs) can increase the performance of the entire framework. Furthermore, implementing a method of evaluating various performance criteria simultaneously, such as efficiency, robustness, and aesthetic quality, allows for artifacts to be evaluated based on the priorities of their creators. Through simulating various scenarios of artifact creation using this new framework, it becomes possible to evaluate the performance of the framework relative to existing methods of artifact creation. As a result, it becomes evident that the framework is able to create artifacts with increased performance optimality compared to existing designs. Furthermore, the framework has the potential to be applied to various different types of artifact creation fields, ranging from engineering to digital art. Thus, this AI-driven framework forms the basis for next-generation AI methods of creating artifacts

Generative Design · Generative grammar · Generative model · Interpretability · Inverse · Aesthetic Perception and Analysis · Generative Adversarial Networks and Image Synthesis · Music Technology and Sound Studies