dc.contributor.advisor | Campagnol, Gabriela | |
dc.contributor.advisor | Shepley, Mardelle M | |
dc.creator | Mullican, Raymond Charles | |
dc.date.accessioned | 2019-01-16T16:59:28Z | |
dc.date.available | 2019-12-01T06:34:29Z | |
dc.date.created | 2017-12 | |
dc.date.issued | 2018-01-19 | |
dc.date.submitted | December 2017 | |
dc.identifier.uri | https://hdl.handle.net/1969.1/173032 | |
dc.description.abstract | With the extended computational limits of algorithmic recursion, scientific investigation is transitioning
away from computationally decidable problems and beginning to address computationally undecidable complexity. The analysis of deductive inference in structure-property models are yielding to the synthesis of inductive inference in process-structure simulations. Process-structure modeling has examined external order parameters of inductive pattern formation, but investigation of the internal order parameters of self-organization have been hampered by the lack of a mathematical formalism with the ability to quantitatively define a specific configuration of points.
This investigation addressed this issue of quantitative synthesis. Local space was developed by the
Poincare inflation of a set of points to construct neighborhood intersections, defining topological distance and introducing situated Boolean topology as a local replacement for point-set topology. Parallel development of the local semi-metric topological space, the local semi-metric probability space, and the local metric space of a set of points provides a triangulation of connectivity measures to define the quantitative architectural identity of a configuration and structure independent axes of a structural configuration space. The recursive sequence of intersections constructs a probabilistic discrete spacetime model of interacting fields to define the internal order parameters of self-organization, with order parameters external to the configuration modeled by adjusting the morphological parameters of individual neighborhoods and the interplay of excitatory and inhibitory point sets. The evolutionary trajectory of a configuration maps the development of specific hierarchical structure that is emergent from a specific set of initial conditions, with nested boundaries signaling the nonlinear properties of local causative configurations. This exploration of architectural configuration space concluded with initial process-structure-property models of deductive and inductive inference spaces.
In the computationally undecidable problem of human niche construction, an adaptive-inductive pattern formation model with predictive control organized the bipartite recursion between an information structure and its physical expression as hierarchical ensembles of artificial neural network-like structures. The union of architectural identity and bipartite recursion generates a predictive structural model of an evolutionary design process, offering an alternative to the limitations of cognitive descriptive modeling. The low computational complexity of these models enable them to be embedded in physical constructions to create the artificial life forms of a real-time autonomously adaptive human habitat. | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.subject | adaptive configuration | en |
dc.subject | adaptive field | en |
dc.subject | architectural configuration space | en |
dc.subject | architectural descriptor | en |
dc.subject | architectural graph | en |
dc.subject | architectural identity | en |
dc.subject | architectural set | en |
dc.subject | architectural trajectory | en |
dc.subject | binding configuration | en |
dc.subject | bipartite recursion | en |
dc.subject | Boolean neighborhood group | en |
dc.subject | Boolean topology | en |
dc.subject | causative configuration | en |
dc.subject | certainty-path graph | en |
dc.subject | completely connected topological space | en |
dc.subject | completely developed space | en |
dc.subject | complex system | en |
dc.subject | configuration | en |
dc.subject | configuration space | en |
dc.subject | connected neighborhood | en |
dc.subject | critical state | en |
dc.subject | deductive inference graph | en |
dc.subject | deductive inference space | en |
dc.subject | evolutionary trajectory | en |
dc.subject | extended genotype | en |
dc.subject | extended phenotype | en |
dc.subject | giant component | en |
dc.subject | human niche construction | en |
dc.subject | in absentia modeling | en |
dc.subject | inductive inference space | en |
dc.subject | internal order parameter | en |
dc.subject | limit-point neighborhood | en |
dc.subject | local phase transition | en |
dc.subject | local probability space | en |
dc.subject | local sample space | en |
dc.subject | locally complete metric space | en |
dc.subject | locally complete probability space | en |
dc.subject | meme | en |
dc.subject | memetic | en |
dc.subject | memotype | en |
dc.subject | non-binding | en |
dc.subject | nonlinear | en |
dc.subject | parallel causation | en |
dc.subject | parallel processing | en |
dc.subject | percolation threshold | en |
dc.subject | Poincare inflation | en |
dc.subject | probabilistic fitness landscape | en |
dc.subject | probability distance | en |
dc.subject | probability-metric index | en |
dc.subject | process-structure–property–performance | en |
dc.subject | process-structure model | en |
dc.subject | quasi-binding configuration | en |
dc.subject | self-assembly | en |
dc.subject | self-organization | en |
dc.subject | self-organized system | en |
dc.subject | semi-metric probability space | en |
dc.subject | semi-metric topological space | en |
dc.subject | situated conditional probability chain | en |
dc.subject | situated connectivity | en |
dc.subject | situated mathematics | en |
dc.subject | situated modeling | en |
dc.subject | situated probability | en |
dc.subject | situated structural modeling | en |
dc.subject | structural coherence | en |
dc.subject | structural phase transition | en |
dc.subject | structure-property model | en |
dc.subject | subjective fitness | en |
dc.subject | topological distance | en |
dc.subject | topological-probability space | en |
dc.subject | topologic-metric index | en |
dc.title | Inductive Pattern Formation | en |
dc.type | Thesis | en |
thesis.degree.department | Architecture | en |
thesis.degree.discipline | Architecture | en |
thesis.degree.grantor | Texas A & M University | en |
thesis.degree.name | Doctor of Philosophy | en |
thesis.degree.level | Doctoral | en |
dc.contributor.committeeMember | Walton, Jay R | |
dc.contributor.committeeMember | Popp, Robert K | |
dc.type.material | text | en |
dc.date.updated | 2019-01-16T16:59:28Z | |
local.embargo.terms | 2019-12-01 | |
local.etdauthor.orcid | 0000-0002-0652-9496 | |