14 research outputs found
A Transfer Operator Methodology for Optimal Sensor Placement Accounting for Uncertainty
Sensors in buildings are used for a wide variety of applications such as
monitoring air quality, contaminants, indoor temperature, and relative
humidity. These are used for accessing and ensuring indoor air quality, and
also for ensuring safety in the event of chemical and biological attacks. It
follows that optimal placement of sensors become important to accurately
monitor contaminant levels in the indoor environment. However, contaminant
transport inside the indoor environment is governed by the indoor flow
conditions which are affected by various uncertainties associated with the
building systems including occupancy and boundary fluxes. Therefore, it is
important to account for all associated uncertainties while designing the
sensor layout. The transfer operator based framework provides an effective way
to identify optimal placement of sensors. Previous work has been limited to
sensor placements under deterministic scenarios. In this work we extend the
transfer operator based approach for optimal sensor placement while accounting
for building systems uncertainties. The methodology provides a probabilistic
metric to gauge coverage under uncertain conditions. We illustrate the
capabilities of the framework with examples exhibiting boundary flux
uncertainty
Physics-aware Deep Generative Models for Creating Synthetic Microstructures
A key problem in computational material science deals with understanding the effect of material distribution (i.e., microstructure) on material performance. The challenge is to synthesize microstructures, given a finite number of microstructure images, and/or some physical invariances that the microstructure exhibits. Conventional approaches are based on stochastic optimization and are computationally intensive. We introduce three generative models for the fast synthesis of binary microstructure images. The first model is a WGAN model that uses a finite number of training images to synthesize new microstructures that weakly satisfy the physical invariances respected by the original data. The second model explicitly enforces known physical invariances by replacing the traditional discriminator in a GAN with an invariance checker. Our third model combines the first two models to reconstruct microstructures that respect both explicit physics invariances as well as implicit constraints learned from the image data. We illustrate these models by reconstructing two-phase microstructures that exhibit coarsening behavior. The trained models also exhibit interesting latent variable interpolation behavior, and the results indicate considerable promise for enforcing user-defined physics constraints during microstructure synthesis
A software framework for data dimensionality reduction: application to chemical crystallography
Materials science research has witnessed an increasing use of data mining techniques in establishing processâstructureâproperty relationships. Significant advances in highâthroughput experiments and computational capability have resulted in the generation of huge amounts of data. Various statistical methods are currently employed to reduce the noise, redundancy, and the dimensionality of the data to make analysis more tractable. Popular methods for reduction (like principal component analysis) assume a linear relationship between the input and output variables. Recent developments in nonâlinear reduction (neural networks, selfâorganizing maps), though successful, have computational issues associated with convergence and scalability. Another significant barrier to use dimensionality reduction techniques in materials science is the lack of ease of use owing to their complex mathematical formulations. This paper reviews various spectralâbased techniques that efficiently unravel linear and nonâlinear structures in the data which can subsequently be used to tractably investigate processâstructureâproperty relationships. In addition, we describe techniques (based on graphâtheoretic analysis) to estimate the optimal dimensionality of the lowâdimensional parametric representation. We show how these techniques can be packaged into a modular, computationally scalable software framework with a graphical user interface â Scalable Extensible Toolkit for Dimensionality Reduction (SETDiR). This interface helps to separate out the mathematics and computational aspects from the materials science applications, thus significantly enhancing utility to the materials science community. The applicability of this framework in constructing reduced order models of complicated materials dataset is illustrated with an example dataset of apatites described in structural descriptor space. Cluster analysis of the lowâdimensional plots yielded interesting insights into the correlation between several structural descriptors like ionic radius and covalence with characteristic properties like apatite stability. This information is crucial as it can promote the use of apatite materials as a potential host system for immobilizing toxic elements
Encoding Invariances in Deep Generative Models
Reliable training of generative adversarial networks (GANs) typically require massive datasets in order to model complicated distributions. However, in several applications, training samples obey invariances that are \textit{a priori} known; for example, in complex physics simulations, the training data obey universal laws encoded as well-defined mathematical equations. In this paper, we propose a new generative modeling approach, InvNet, that can efficiently model data spaces with known invariances. We devise an adversarial training algorithm to encode them into data distribution. We validate our framework in three experimental settings: generating images with fixed motifs; solving nonlinear partial differential equations (PDEs); and reconstructing two-phase microstructures with desired statistical properties. We complement our experiments with several theoretical results