Bioinspired design in architecture has long relied on algorithmic abstractions of natural systems — from branching patterns in L-systems to evolutionary and swarm-based optimization models. While these approaches have generated visually compelling structures, they tend to reduce the richness of biological intelligence into symbolic and deterministic operations. This results in a design culture that often privileges formal mimicry over behavioral fidelity.
This paper deepens that critique by moving beyond comparisons of specific algorithmic models to address a broader epistemological issue: the reduction of biological processes into computational schemas that favor visual replication over behavioral understanding. Architectural applications of bioinspired algorithms often operate through linear logic, rule-based control, and predefined evaluation criteria—abstracting the dynamics of natural systems into deterministic, formal outputs. In doing so, they overlook key attributes of biological intelligence such as contextual feedback, uncertainty, and adaptive responsiveness. These characteristics are not peripheral but central to the generative logic of living systems, and should form the basis of biomimetic integration in architectural design.
To propose an alternative, this research explores AI-driven inference models — such as generative diffusion networks, flow-based embeddings, and latent space interpolations — as methods to reconstruct biologically-informed spatial behavior without relying on prescriptive rules. These models are trained on curated datasets of biological morphologies and movement patterns to extract underlying logics rather than static appearances.
Two design experiments are presented: one at the pavilion scale, where form growth is compared between L-system/simulation-based shells and diffusion-informed membranes; and one at the floor plan level, where conventional GA-based spatial optimization is contrasted with AI-inferred layout generation based on behavioral flow. Visual mappings and critical diagrams serve to unpack how each approach encodes growth, constraint, and adaptation.
Ultimately, this paper advances a speculative yet grounded framework for post-algorithmic bioinspired design, in which architecture moves beyond coding nature’s image toward learning from its intelligence through holistic reverse engineering. It invites a methodological shift — from designing with symbolic algorithms toward designing with learned biologic behavior, unlocking new potentials for form, responsiveness, and meaning in architectural computation.
