3,588 research outputs found
IH-GAN: A Conditional Generative Model for Implicit Surface-Based Inverse Design of Cellular Structures
Variable-density cellular structures can overcome connectivity and
manufacturability issues of topologically optimized structures, particularly
those represented as discrete density maps. However, the optimization of such
cellular structures is challenging due to the multiscale design problem. Past
work addressing this problem generally either only optimizes the volume
fraction of single-type unit cells but ignoring the effects of unit cell
geometry on properties, or considers the geometry-property relation but builds
this relation via heuristics. In contrast, we propose a simple yet more
principled way to accurately model the property to geometry mapping using a
conditional deep generative model, named Inverse Homogenization Generative
Adversarial Network (IH-GAN). It learns the conditional distribution of unit
cell geometries given properties and can realize the one-to-many mapping from
geometry to properties. We further reduce the complexity of IH-GAN by using the
implicit function parameterization to represent unit cell geometries. Results
show that our method can 1) generate various unit cells that satisfy given
material properties with high accuracy (relative error <5%) and 2) improve the
optimized structural performance over the conventional topology-optimized
variable-density structure. Specifically, in the minimum compliance example,
our IH-GAN generated structure achieves an 84.4% reduction in concentrated
stress and an extra 7% reduction in displacement. In the target deformation
examples, our IH-GAN generated structure reduces the target matching error by
24.2% and 44.4% for two test cases, respectively. We also demonstrated that the
connectivity issue for multi-type unit cells can be solved by transition layer
blending
Deep learning in computational microscopy
We propose to use deep convolutional neural networks (DCNNs) to perform 2D and 3D computational imaging. Specifically, we investigate three different applications. We first try to solve the 3D inverse scattering problem based on learning a huge number of training target and speckle pairs. We also demonstrate a new DCNN architecture to perform Fourier ptychographic Microscopy (FPM) reconstruction, which achieves high-resolution phase recovery with considerably less data than standard FPM. Finally, we employ DCNN models that can predict focused 2D fluorescent microscopic images from blurred images captured at overfocused or underfocused planes.Published versio
Scalable Population Synthesis with Deep Generative Modeling
Population synthesis is concerned with the generation of synthetic yet
realistic representations of populations. It is a fundamental problem in the
modeling of transport where the synthetic populations of micro-agents represent
a key input to most agent-based models. In this paper, a new methodological
framework for how to 'grow' pools of micro-agents is presented. The model
framework adopts a deep generative modeling approach from machine learning
based on a Variational Autoencoder (VAE). Compared to the previous population
synthesis approaches, including Iterative Proportional Fitting (IPF), Gibbs
sampling and traditional generative models such as Bayesian Networks or Hidden
Markov Models, the proposed method allows fitting the full joint distribution
for high dimensions. The proposed methodology is compared with a conventional
Gibbs sampler and a Bayesian Network by using a large-scale Danish trip diary.
It is shown that, while these two methods outperform the VAE in the
low-dimensional case, they both suffer from scalability issues when the number
of modeled attributes increases. It is also shown that the Gibbs sampler
essentially replicates the agents from the original sample when the required
conditional distributions are estimated as frequency tables. In contrast, the
VAE allows addressing the problem of sampling zeros by generating agents that
are virtually different from those in the original data but have similar
statistical properties. The presented approach can support agent-based modeling
at all levels by enabling richer synthetic populations with smaller zones and
more detailed individual characteristics.Comment: 27 pages, 15 figures, 4 table
Nature as a Network of Morphological Infocomputational Processes for Cognitive Agents
This paper presents a view of nature as a network of infocomputational agents organized in a dynamical hierarchy of levels. It provides a framework for unification of currently disparate understandings of natural, formal, technical, behavioral and social phenomena based on information as a structure, differences in one system that cause the differences in another system, and computation as its dynamics, i.e. physical process of morphological change in the informational structure. We address some of the frequent misunderstandings regarding the natural/morphological computational models and their relationships to physical systems, especially cognitive systems such as living beings. Natural morphological infocomputation as a conceptual framework necessitates generalization of models of computation beyond the traditional Turing machine model presenting symbol manipulation, and requires agent-based concurrent resource-sensitive models of computation in order to be able to cover the whole range of phenomena from physics to cognition. The central role of agency, particularly material vs. cognitive agency is highlighted
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