19 research outputs found
Finite Element Modeling of Transverse Post-Tensioned Joints in Accelerated Bridge Construction (ABC) Full-Scale Precast Bridge Deck Panels
The Accelerated bridge construction (ABC) techniques are gaining popularity among the departments of transportation (DOTs) due to their reductions of on-site construction time and traffic delays. One ABC technique that utilizes precast deck panels has demonstrated some advantages over normal cast-in-place construction, but has also demonstrated some serviceability issues such as cracks and water leakage to the transverse joints. Some of these problems are addressed by applying longitudinal prestressing. This thesis evaluates the service and ultimate capacities in both flexure and shear, of the finite element models of the post-tensioned system currently used by Utah Department of Transportation (UDOT) and a proposed curved-bolt system to confirm the experimental results. The panels were built and tested under negative moment in order to investigate a known problem, namely, tension in the deck concrete. Shear tests were performed on specimens with geometry designed to investigate the effects of high shear across the joint. The curved-bolt connection not only provides the necessary compressive stress across the transverse joint but also makes future replacement of a single deck panel possible without replacing the entire deck. Load-deflection, shear-deflection curves were obtained using the experimental tests and were used to compare with the values obtained from finite element analysis. In flexure, the ultimate load predicted by the finite element model was lower than the experimental ultimate load by 1% for the post-tensioned connection and 3% for the curved-bolt connection. The shear models predicted the ultimate shear reached, within 5% of the experimental values. The cracking pattern also matched closely. The yield and cracking moment of the curved-bolt connection predicted by the finite element model were lower by 13% and 2%, respectively, compared to the post-tensioned connection in flexure
Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning
The ability to learn and adapt in real time is a central feature of
biological systems. Neuromorphic architectures demonstrating such versatility
can greatly enhance our ability to efficiently process information at the edge.
A key challenge, however, is to understand which learning rules are best suited
for specific tasks and how the relevant hyperparameters can be fine-tuned. In
this work, we introduce a conceptual framework in which the learning process is
integrated into the network itself. This allows us to cast meta-learning as a
mathematical optimization problem. We employ DeepHyper, a scalable,
asynchronous model-based search, to simultaneously optimize the choice of
meta-learning rules and their hyperparameters. We demonstrate our approach with
two different datasets, MNIST and FashionMNIST, using a network architecture
inspired by the learning center of the insect brain. Our results show that
optimal learning rules can be dataset-dependent even within similar tasks. This
dependency demonstrates the importance of introducing versatility and
flexibility in the learning algorithms. It also illuminates experimental
findings in insect neuroscience that have shown a heterogeneity of learning
rules within the insect mushroom body
Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness
Robust machine learning models with accurately calibrated uncertainties are
crucial for safety-critical applications. Probabilistic machine learning and
especially the Bayesian formalism provide a systematic framework to incorporate
robustness through the distributional estimates and reason about uncertainty.
Recent works have shown that approximate inference approaches that take the
weight space uncertainty of neural networks to generate ensemble prediction are
the state-of-the-art. However, architecture choices have mostly been ad hoc,
which essentially ignores the epistemic uncertainty from the architecture
space. To this end, we propose a Unified probabilistic architecture and weight
ensembling Neural Architecture Search (UraeNAS) that leverages advances in
probabilistic neural architecture search and approximate Bayesian inference to
generate ensembles form the joint distribution of neural network architectures
and weights. The proposed approach showed a significant improvement both with
in-distribution (0.86% in accuracy, 42% in ECE) CIFAR-10 and
out-of-distribution (2.43% in accuracy, 30% in ECE) CIFAR-10-C compared to the
baseline deterministic approach
Towards Continually Learning Application Performance Models
Machine learning-based performance models are increasingly being used to
build critical job scheduling and application optimization decisions.
Traditionally, these models assume that data distribution does not change as
more samples are collected over time. However, owing to the complexity and
heterogeneity of production HPC systems, they are susceptible to hardware
degradation, replacement, and/or software patches, which can lead to drift in
the data distribution that can adversely affect the performance models. To this
end, we develop continually learning performance models that account for the
distribution drift, alleviate catastrophic forgetting, and improve
generalizability. Our best model was able to retain accuracy, regardless of
having to learn the new distribution of data inflicted by system changes, while
demonstrating a 2x improvement in the prediction accuracy of the whole data
sequence in comparison to the naive approach.Comment: Presented at Workshop on Machine Learning for Systems at 36th
Conference on Neural Information Processing Systems (NeurIPS 2022
A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling
Upcoming large astronomical surveys are expected to capture an unprecedented
number of strong gravitational lensing systems in the Universe. Deep learning
is emerging as a promising practical tool in detection and quantification of
these galaxy-scale image distortions. However, absence of large quantities of
representative data from current astronomical surveys requires development of
robust forward modeling of synthetic lensing images. Using a realistic and
unbiased sample of the strong lenses created by using state-of-the-art
extragalactic catalogs, we train a modular deep learning pipeline for
uncertainty-quantified detection and modeling with intermediate image
processing components for denoising and deblending the lensing systems. We
demonstrate a higher degree of interpretability and controlled systematics due
to domain-specific task modules that are trained with different stages of
synthetic image generation. For lens detection and modeling, we obtain
semantically meaningful latent spaces that separate classes and provide
uncertainty estimates that explain the misclassified images and provide
uncertainty bounds on the lens parameters. In addition, we obtain an improved
performance---lens detection (classification) improved from 82% with the
baseline to 94%, while the lens modeling (regression) accuracy improved by 25%
over the baseline model
Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck
The information bottleneck framework provides a systematic approach to
learning representations that compress nuisance information in the input and
extract semantically meaningful information about predictions. However, the
choice of a prior distribution that fixes the dimensionality across all the
data can restrict the flexibility of this approach for learning robust
representations. We present a novel sparsity-inducing spike-slab categorical
prior that uses sparsity as a mechanism to provide the flexibility that allows
each data point to learn its own dimension distribution. In addition, it
provides a mechanism for learning a joint distribution of the latent variable
and the sparsity and hence can account for the complete uncertainty in the
latent space. Through a series of experiments using in-distribution and
out-of-distribution learning scenarios on the MNIST, CIFAR-10, and ImageNet
data, we show that the proposed approach improves accuracy and robustness
compared to traditional fixed-dimensional priors, as well as other sparsity
induction mechanisms for latent variable models proposed in the literature