Deep learning surrogate models for spatial and visual connectivity

Spatial and visual connectivity are important metrics when developing workplace layouts. Calculating those metrics in real time can be difficult, depending on the size of the floor plan being analysed and the resolution of the analyses. This article investigates the possibility of considerably speeding up the outcomes of such computationally intensive simulations by using machine learning to create models capable of identifying the spatial and visual connectivity potential of a space. To that end, we present the entire process of investigating different machine learning models and a pipeline for training them on such task, from the incorporation of a bespoke spatial and visual connectivity analysis engine through a distributed computation pipeline, to the process of synthesizing training data and evaluating the performance of different neural networks

Spatial Solutions and Solution Spaces: The use of Virtual and Augmented Reality in Design Exploration

The recent wave of Virtual and Augmented Reality (VAR) technologies has coincided with new technologies for processing, analyzing and evaluating large amounts of data. In general, the purpose of Data Visualization is to enable the user to discover and understand patterns in data. Good visualizations present large amounts of data in a way that is easily understood, and good interactive visualizations promote intuitive means of exploring relationships. Over the past few years many researchers have looked into the development of immersive Virtual Environment platforms for Big Data visualization, such as, iViz (Donalek et al, 2014) and the work carried out by Masters of Pie and Lumacode for the Big Data VR Challenge in 2016 (Lumapie, 2016). Filtering, combination and scaling have all been identified elsewhere as important interactive techniques used in contemporary data visualization (Olshannikova et al, 2015). Of these, scaling may be the most familiar to architects: for centuries, designers have attempted to experience architectural space in different scales simultaneously, by using models at different scales (Yaneva, 2005), and by employing various drawing techniques to achieve an embodied perception of the designed space. With the use of VAR technologies this becomes easier than ever. At the same time, designers increasingly must understand not just the experience of a design proposal but also the data associated with it

Design of thermally deformable laminates using machine learning

Recent advances in material science and manufacturing have enabled designers to create objects which respond to changing environmental conditions by controlled deformation, without external mechanical or electrical actuation. The design of such smart materials has mostly been done through trial and error due to their complex nonlinear behavior. This paper will present how this problem is addressed by introducing a novel inverse design workflow. In this, a desired structural deformation is used as an input to a machine learned model which subsequently outputs the required geometric and material properties that will produce said deformation when exposed to an external stimulus. This workflow uses a Generative Adversarial Neural Network (GANN) trained on pairs of input cut-out patterns of laminate layers and their nonlinear Finite Element Analysis (FEA) simulation results. The method offers a significant performance speed-up while maintaining acceptable levels of accuracy, especially at the early design stage. This methodology could be further extended to the design of any nonlinear mechanical deformation

Form finding nodal connections in grid structure

Nodes for grid structures are often manufactured in a rather material intensive and inefficient way, increasing the weight of the structure and thus the load. Recent development of additive manufacturing techniques, have resulted in a rising interest in large-scale metal 3D printing. Topology optimization has become the obvious companion in the design of structural parts for 3D printing, and rightfully so. The technique is demonstrably able to provide material efficient solutions and is well suited for a manufacturing technique with few formal restrictions. However, from a designer’s perspective one could argue that topology optimization have some limitations. Like other “automated processes”, it tends to take over and does not leave much room for other form drivers. This paper presents an alternative method for designing material efficient nodes in grid structures that builds on the conventional form-finding techniques, usually applied to create minimal surface tensile structures or gravity shell like structures. The technique works by modelling the node as a hollow shell with a mesh, applying a set of tensile forces derived from the structural action from elements adjacent to the node (where compression is converted to tension) and running a form finding simulation. After the simulation, the shell is then thickened and analysed for the real load case (which consider both tension and compression) using FE-analysis. The benefit of such technique is that the designer has control over the topology of the design which enables more creative control and free exploration of a range of design variations. The form finding is done using dynamic relaxation and introduces spline elements with bending capability to control deviation from the pure spring network solution

Façade apertures optimization: integrating cross-ventilation performance analysis in fluid dynamics simulation

Performance-oriented design has as a primary aim to introduce spaces that achieve acceptable levels of human comfort. Wind-induced airflow plays a significant role in the improving occupants' comfort in a building. This paper explores the extent to which simulation of natural airflow can potentially be a contributing parameter in the conception of performance-aware designs. Testing the natural ventilation performance of a pavilion, the study employs Fast Fluid Dynamics simulation. A performance analysis is conducted, whereby an array of automated feedback loops carried out by a genetic algorithm can produce a number of acceptable solutions as regards the optimization of facades' openings. The experimentation conducted proves the ability of the model to yield design instances that fulfill a number of environmental criteria related to airflow and human comfort. In this light, the paper suggests that the aforementioned method can be used as an experimentation platform to influence the direction a designer may take when considering a design proposal

14-3-3ε Is Required for Germ Cell Migration in Drosophila

Although 14-3-3 proteins participate in multiple biological processes, isoform-specific specialized functions, as well as functional redundancy are emerging with tissue and developmental stage-specificity. Accordingly, the two 14-3-3ε proteins in Drosophila exhibit functional specificity and redundancy. Homozygotes for loss of function alleles of D14-3-3ε contain significantly fewer germ line cells (pole cells) in their gonads, a phenotype not shared by mutants in the other 14-3-3 gene leo. We show that although D14-3-3ε is enriched within pole cells it is required in mesodermal somatic gonad precursor cells which guide pole cells in their migration through the mesoderm and coalesce with them to form the embryonic gonad. Loss of D14-3-3ε results in defective pole cell migration, reduced pole cell number. We present evidence that D14-3-3ε loss results in reduction or loss of the transcription factor Zfh-1, one of the main regulatory molecules of the pole cell migration, from the somatic gonad precursor cells

Just Passing Through: Integration in Computational Environmental Design

This paper proposes Buckminster Fuller's concept of pattern integrity as a context for understanding computational techniques in environmentally responsive design. We argue that successful integration in this context requires a continuous design medium that allows for heterogeneous, mutable techniques and models. This model of integration is demonstrated by reference to a current project for a large canopy structure in Singapore with specific focus on issues of environmental mediation, object-oriented programming for CAD environments, and functional programming techniques within parametric modeling systems. We discuss the applicability of these novel integrative approaches to wider problems in computational design

14-3-3ζ-Leo over-expression does not rescue pole cell number in <i>D14-3-3ε^ex4</i>homozygotes.

<p>Expression of the two ubiquitous Leo isoforms, LeoI and LeoII <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036702#pone.0036702-Messaritou1" target="_blank">[12]</a> in mutant homozygotes under Tub-Gal4, Act-Gal4 and NosVp16-Gal4 (light gray bars) failed to change the reduced number of pole cells in <i>D14-3-3ε^ex4</i> homozygotes. The dark bar indicates the number of pole cells per gonad in <i>D14-3-3ε^ex4</i> homozygotes. The number of pole cells in embryos expressing transgenes (light gray bars) was compared to that of the homozygotes (dark gray bar) and of genotype-matched heterozygotes (medium gray bars) carrying each UAS transgene on the same chromosome as that bearing the <i>D14-3-3ε^ex4</i> mutation. Full rescue was attained only when Tub-Gal4 drove the UAS<i>epsilon</i> (UAS<i>eps</i>) transgene. The line is drawn to aid comparisons with the number of pole cells in the mutant homozygotes.</p

<i>D14-3-3ε</i> mutants are sterile.

<p>The number of single crosses that yielded larvae (% Fertile) over the total number of animals crossed (# crossed) per genotype is reported. D14<i>-3-3ε^ex5</i> are used as controls since they have the same genetic background as the mutants and express normal amounts of the protein <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036702#pone.0036702-Acevedo1" target="_blank">[13]</a>.</p