Publications

Conference Proceedings of the Society for Experimental Mechanics Series

Jacobs, Justin; Najera-Flores, David A.; Brink, Adam R.; Flanagan, Tatiana P.

Complex aerospace structures typically include unknown states, parameters, or inputs. The unknown parameters may be due to changes in the structure that are not captured by the mathematical model assumed. These models are often reduced order models (ROM) that have simplified physics or have been obtained through data-driven techniques, such as trained neural networks. In this paper, we evaluate two data assimilation techniques to perform parameter estimation of dynamical systems by leveraging measured responses to correct process model predictions. We study two different noise models: discontinuous and continuous Gaussian noises. We use ensemble Kalman filter and Kalman-Bucy filter techniques on representative structures, such as the slender flat beam with nonlinear features to illustrate how this approach could be applied to more complex structures.

Stamber, Kevin L.; Aamir, Munaf S.; Beyeler, Walter E.; Brown, Theresa J.; Bynum, Leo B.; Corbet, Thomas F.; Flanagan, Tatiana P.; Kelic, Andjelka; Pate, Ronald P.; Tenney, Craig M.; Tidwell, Vincent C.

Electric power is crucial to the function of other infrastructures, as well as to the stability of the economy and the social order. Disruption of commercial electric power service, even for brief periods of time, can create significant consequences to the function of other sectors, and make living in some environments untenable. This analysis, conducted in 2017 for the United States Department of Energy (DOE) as part of the Grid Modernization Laboratory Consortium (GMLC) Initiative, focuses on describing the function of each of the other infrastructure sectors and subsectors, with an eye towards those elements of these sectors that depend on primary electric power service through the commercial electric power grid. It leverages the experience of Sandia analysts in analyzing historical disruptive events, and from the development of capabilities designed to identify the physical, logical, and geographic connectivity between infrastructures. The analysis goes on to identify alternatives for the provision of primary electric power service, and the redundancy of said alternatives, to provide a picture of the sector’s ability to withstand an extended disruption.

Springer Proceedings in Complexity

Flanagan, Tatiana P.; Beyeler, Walter E.; Levin, Drew L.; Finley, Patrick D.; Moses, Melanie

Data obtained from biosurveillance can be used by public health systems to detect and respond to disease outbreaks and save lives. However, existing data is distributed across large geographic areas, and both the quality and type of data vary in space and time. We discuss a framework for analyzing biosurveillance information to minimize detection time and maximize detection accuracy while scaling the analysis over large regions. We propose that strategies used by canonical biological complex systems, which are adapted to diverse environments, provide good models for the design of a robust, adaptive, and scalable biosurveillance system. Drawing from knowledge of the adaptive immune system, and ant colonies, we examine strategies that support the scaling of detection in order to search and respond in large areas with dynamic distributions of data. Based on this research, we discuss a bioinspired approach for a distributed, adaptive, and scalable biosurveillance system.

Finley, Patrick D.; Finley, Patrick D.; Finley, Patrick D.; Finley, Patrick D.; Levin, Drew L.; Levin, Drew L.; Levin, Drew L.; Levin, Drew L.; Flanagan, Tatiana P.; Flanagan, Tatiana P.; Flanagan, Tatiana P.; Flanagan, Tatiana P.; Beyeler, Walter E.; Beyeler, Walter E.; Beyeler, Walter E.; Beyeler, Walter E.; Mitchell, Michael D.; Mitchell, Michael D.; Mitchell, Michael D.; Mitchell, Michael D.; Ray, Jaideep R.; Ray, Jaideep R.; Ray, Jaideep R.; Ray, Jaideep R.; Moses, Melanie M.; Moses, Melanie M.; Moses, Melanie M.; Moses, Melanie M.; Forrest, Stephanie F.; Forrest, Stephanie F.; Forrest, Stephanie F.; Forrest, Stephanie F.

This study developed and tested biologically inspired computational methods to detect anomalous signals in data streams that could indicate a pending outbreak or bio-weapon attack. Current large- scale biosurveillance systems are plagued by two principal deficiencies: (1) timely detection of disease-indicating signals in noisy data and (2) anomaly detection across multiple channels. Anomaly detectors and data fusion components modeled after human immune system processes were tested against a variety of natural and synthetic surveillance datasets. A pilot scale immune-system-based biosurveillance system performed at least as well as traditional statistical anomaly detection data fusion approaches. Machine learning approaches leveraging Deep Learning recurrent neural networks were developed and applied to challenging unstructured and multimodal health surveillance data. Within the limits imposed of data availability, both immune systems and deep learning methods were found to improve anomaly detection and data fusion performance for particularly challenging data subsets. ACKNOWLEDGEMENTS The authors acknowledge the close collaboration of Scott Lee, Jason Thomas, and Chad Heilig from the US Centers for Disease Control (CDC) in this effort. De-identified biosurveillance data provided by Ken Jeter of the New Mexico Department of Health proved to be an important contribution to our work. Discussions with members of the International Society of Disease Surveillance helped the researchers focus on questions relevant to practicing public health professionals. Funding for this work was provided by Sandia National Laboratories' Laboratory Directed Research and Development program.

2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings

Levin, Drew L.; Moses, Melanie; Flanagan, Tatiana P.; Forrest, Stephanie; Finley, Patrick D.

Early detection of emerging disease outbreaks is crucial to effective containment and response, yet initial outbreak signatures can be difficult to detect with automated methods. Outbreaks may be masked by noisy data, and signs of an outbreak may be hidden across multiple data feeds. Current biosurveillance methods often perform unimodal statistical analyses that are unable to intelligently leverage multiple correlated data of different types while still retaining quantitative sensitivity. In this paper, we propose and implement an anomaly detection system for health data based upon the human immune system. The adaptive immune system operates over a high-dimensional antigen space in a distributed manner, allowing it to efficiently scale without relying on a centralized controller. Our negative selection algorithm based on the immune system provides effective and scalable distributed anomaly detection for biosurveillance. It detects anomalies in the large, complex data from modern health monitoring data feeds with low false positive rates. Our bootstrap aggregation method improves performance on high-dimensional data sets, and we implement a parallelized version of the algorithm to demonstrate the potential to implement it on a scalable distributed architecture. Our negative selection algorithm is able to detect 90% of all outbreaks with a false positive rate of 11.8% in a publicly available multimodal synthetic health record data set. The scalability and performance of the negative selection algorithm demonstrate that immune computation can provide effective approaches for national and global scale biosurveillence.

Reliability Engineering and System Safety

Corbet, Thomas F.; Beyeler, Walter E.; Wilson, Michael L.; Flanagan, Tatiana P.

Simulation models can improve decisions meant to control the consequences of disruptions to critical infrastructures. We describe a dynamic flow model on networks purposed to inform analyses by those concerned about consequences of disruptions to infrastructures and to help policy makers design robust mitigations. We conceptualize the adaptive responses of infrastructure networks to perturbations as market transactions and business decisions of operators. We approximate commodity flows in these networks by a diffusion equation, with nonlinearities introduced to model capacity limits. To illustrate the behavior and scalability of the model, we show its application first on two simple networks, then on petroleum infrastructure in the United States, where we analyze the effects of a hypothesized earthquake.

DeRosa, Sean D.; Flanagan, Tatiana P.

Crude oil produced on the North Slope of Alaska (NSA) is primarily transported on the Trans-Alaska Pipeline System (TAPS) to in-state refineries and the Valdez Marine Terminal in southern Alaska. From the Terminal, crude oil is loaded onto tankers and is transported to export markets or to three major locations along the U.S. West Coast: Anacortes-Ferndale area (Washington), San Francisco Bay area, and Los Angeles area. North Slope of Alaska production has decreased about 75% since the 1980s, which has reduced utilization of TAPS.

DeRosa, Sean D.; Flanagan, Tatiana P.

The National Transportation Fuels Model was used to simulate a hypothetical increase in North Slope of Alaska crude oil production. The results show that the magnitude of production utilized depends in part on the ability of crude oil and refined products infrastructure in the contiguous United States to absorb and adjust to the additional supply. Decisions about expanding North Slope production can use the National Transportation Fuels Model take into account the effects on crude oil flows in the contiguous United States.

Vugrin, Eric D.; Trucano, Timothy G.; Swiler, Laura P.; Finley, Patrick D.; Flanagan, Tatiana P.; Naugle, Asmeret B.; Tsao, Jeffrey Y.; Verzi, Stephen J.

Improved validation for models of complex systems has been a primary focus over the past year for the Resilience in Complex Systems Research Challenge. This document describes a set of research directions that are the result of distilling those ideas into three categories of research -- epistemic uncertainty, strong tests, and value of information. The content of this document can be used to transmit valuable information to future research activities, update the Resilience in Complex Systems Research Challenge's roadmap, inform the upcoming FY18 Laboratory Directed Research and Development (LDRD) call and research proposals, and facilitate collaborations between Sandia and external organizations. The recommended research directions can provide topics for collaborative research, development of proposals, workshops, and other opportunities.

Kinnan, Mark K.; Valerio, Richard A.; Flanagan, Tatiana P.; Shirah, Donald N.

This report gives introductory guidance on the level of effort required to create a data warehouse for mining data. Numerous tutorials have been provided to demonstrate the process of downloading raw data, processing the raw data, and importing the data into a PostgreSQL database. Additional information and tutorial has been provided on setting up a Hadoop cluster for storing vasts amounts of data. This report has been generated as a deliverable for a New Mexico Small Business Assistance (NMSBA) project.

Tsao, Jeffrey Y.; Trucano, Timothy G.; Kleban, S.D.; Naugle, Asmeret B.; Verzi, Stephen J.; Swiler, Laura P.; Johnson, Curtis M.; Smith, Mark A.; Flanagan, Tatiana P.; Vugrin, Eric D.; Gabert, Kasimir G.; Lave, Matthew S.; Chen, Wei C.; DeLaurentis, Daniel D.; Hubler, Alfred H.; Oberkampf, Bill O.

This report contains the written footprint of a Sandia-hosted workshop held in Albuquerque, New Mexico, June 22-23, 2016 on “Complex Systems Models and Their Applications: Towards a New Science of Verification, Validation and Uncertainty Quantification,” as well as of pre-work that fed into the workshop. The workshop’s intent was to explore and begin articulating research opportunities at the intersection between two important Sandia communities: the complex systems (CS) modeling community, and the verification, validation and uncertainty quantification (VVUQ) community The overarching research opportunity (and challenge) that we ultimately hope to address is: how can we quantify the credibility of knowledge gained from complex systems models, knowledge that is often incomplete and interim, but will nonetheless be used, sometimes in real-time, by decision makers?