The Opportunity and Challenge of Serverless Computing and Large Scale Computational Science
Abstract not provided.
Publications
FY20 CSSE L2 Milestone 7186
Abstract not provided.
Sensitivity and Uncertainty Workflow of Full System SIERRA Models Supporting High Consequence Applications
Abstract not provided.
Applying executable graphical workflows in SAW to electrical simulation-based analysis
Abstract not provided.
Simple effective conservative treatment of uncertainty from sparse samples of random functions
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems. Part B. Mechanical Engineering
This paper examines the variability of predicted responses when multiple stress-strain curves (reflecting variability from replicate material tests) are propagated through a finite element model of a ductile steel can being slowly crushed. Over 140 response quantities of interest (including displacements, stresses, strains, and calculated measures of material damage) are tracked in the simulations. Each response quantity’s behavior varies according to the particular stress-strain curves used for the materials in the model. We desire to estimate response variability when only a few stress-strain curve samples are available from material testing. Propagation of just a few samples will usually result in significantly underestimated response uncertainty relative to propagation of a much larger population that adequately samples the presiding random-function source. A simple classical statistical method, Tolerance Intervals, is tested for effectively treating sparse stress-strain curve data. The method is found to perform well on the highly nonlinear input-to-output response mappings and non-standard response distributions in the can-crush problem. The results and discussion in this paper support a proposition that the method will apply similarly well for other sparsely sampled random variable or function data, whether from experiments or models. Finally, the simple Tolerance Interval method is also demonstrated to be very economical.
NextGen Workflow for Electrical Analysis Calibration
Abstract not provided.
Cantilever Beam End-to-End UQ Test Problem and Evaluation Criteria for UQ Methods Performance Assessment
Abstract not provided.
Analyst-to-Analyst Variability in Simulation-Based Prediction
This report describes findings from the culminating experiment of the LDRD project entitled, "Analyst-to-Analyst Variability in Simulation-Based Prediction". For this experiment, volunteer participants solving a given test problem in engineering and statistics were interviewed at different points in their solution process. These interviews are used to trace differing solutions to differing solution processes, and differing processes to differences in reasoning, assumptions, and judgments. The issue that the experiment was designed to illuminate -- our paucity of understanding of the ways in which humans themselves have an impact on predictions derived from complex computational simulations -- is a challenging and open one. Although solution of the test problem by analyst participants in this experiment has taken much more time than originally anticipated, and is continuing past the end of this LDRD, this project has provided a rare opportunity to explore analyst-to-analyst variability in significant depth, from which we derive evidence-based insights to guide further explorations in this important area.
Evaluation of a Simple UQ Approach for Sparse Stress-Strain Curve Data in Solid Mechanics Applications
Abstract not provided.
Evaluation of a Simple UQ Approach to Compensate for Sparse Stress-Strain Curve Data in Solid Mechanics Applications
Abstract not provided.
Some Test Problems and Results in Assessing Methods for Calculating Low Probabilities of Failure
Abstract not provided.
Simulating Behavioral Responses in International Security Settings
Abstract not provided.
A data driven approach to assess team performance through team communication
Abstract not provided.
A Data-Driven Approach to Assessing Team Performance through Team Communication
Abstract not provided.
Human factors in the design of a search tool for a database of recorded human behavior
Advances in Cognitive Engineering and Neuroergonomics
Many enterprises are becoming increasingly data-driven. For example, empirically collected data about customer behavior offers an alternative to more traditional, synthetic techniques such as surveys, focus groups, and subject-matter experts. In contrast, recordings of tactical training exercises for the US military are not broadly archived or available for analysis. There may be great opportunity for military training and planning to use analogous techniques, where tactical scenarios are systematically recorded, indexed, and archived. Such a system would provide information for all levels of analysis, including establishing benchmarks for individual performance, evaluating the relevance and impact of training protocols, and assessing the utility of proposed systems and conops. However, such a system also offers many challenges and risks, such as cost, security, privacy, and end-user accessibility. This paper examines the possible benefits and risks of such a system with some emphasis on our recent research to address end-user accessibility.
Using a hybrid cognitive-system dynamics model to anticipate the influence of events and actions on human behaviors
Abstract not provided.
Using a Hybrid Cognitive-System Dynamics Model to Anticipate the Influence of Events and Actions on Human Behaviors
Abstract not provided.
Real-time individualized training vectors for experiential learning
Military training utilizing serious games or virtual worlds potentially generate data that can be mined to better understand how trainees learn in experiential exercises. Few data mining approaches for deployed military training games exist. Opportunities exist to collect and analyze these data, as well as to construct a full-history learner model. Outcomes discussed in the present document include results from a quasi-experimental research study on military game-based experiential learning, the deployment of an online game for training evidence collection, and results from a proof-of-concept pilot study on the development of individualized training vectors. This Lab Directed Research & Development (LDRD) project leveraged products within projects, such as Titan (Network Grand Challenge), Real-Time Feedback and Evaluation System, (America's Army Adaptive Thinking and Leadership, DARWARS Ambush! NK), and Dynamic Bayesian Networks to investigate whether machine learning capabilities could perform real-time, in-game similarity vectors of learner performance, toward adaptation of content delivery, and quantitative measurement of experiential learning.
Trainable automated forces
Abstract not provided.
Enabling immersive simulation
The object of the 'Enabling Immersive Simulation for Complex Systems Analysis and Training' LDRD has been to research, design, and engineer a capability to develop simulations which (1) provide a rich, immersive interface for participation by real humans (exploiting existing high-performance game-engine technology wherever possible), and (2) can leverage Sandia's substantial investment in high-fidelity physical and cognitive models implemented in the Umbra simulation framework. We report here on these efforts. First, we describe the integration of Sandia's Umbra modular simulation framework with the open-source Delta3D game engine. Next, we report on Umbra's integration with Sandia's Cognitive Foundry, specifically to provide for learning behaviors for 'virtual teammates' directly from observed human behavior. Finally, we describe the integration of Delta3D with the ABL behavior engine, and report on research into establishing the theoretical framework that will be required to make use of tools like ABL to scale up to increasingly rich and realistic virtual characters.
EEG analyses with SOBI
The motivating vision behind Sandia's MENTOR/PAL LDRD project has been that of systems which use real-time psychophysiological data to support and enhance human performance, both individually and of groups. Relevant and significant psychophysiological data being a necessary prerequisite to such systems, this LDRD has focused on identifying and refining such signals. The project has focused in particular on EEG (electroencephalogram) data as a promising candidate signal because it (potentially) provides a broad window on brain activity with relatively low cost and logistical constraints. We report here on two analyses performed on EEG data collected in this project using the SOBI (Second Order Blind Identification) algorithm to identify two independent sources of brain activity: one in the frontal lobe and one in the occipital. The first study looks at directional influences between the two components, while the second study looks at inferring gender based upon the frontal component.
Modeling Populations of Interest in Order to Simulate Cultural Response to Influence Activities
Abstract not provided.
Preparing for the Aftermath: Using Emotional Agents in Game-Based Training for Disaster Response
Abstract not provided.
Simulating human behavior for national security human interactions
This 3-year research and development effort focused on what we believe is a significant technical gap in existing modeling and simulation capabilities: the representation of plausible human cognition and behaviors within a dynamic, simulated environment. Specifically, the intent of the ''Simulating Human Behavior for National Security Human Interactions'' project was to demonstrate initial simulated human modeling capability that realistically represents intra- and inter-group interaction behaviors between simulated humans and human-controlled avatars as they respond to their environment. Significant process was made towards simulating human behaviors through the development of a framework that produces realistic characteristics and movement. The simulated humans were created from models designed to be psychologically plausible by being based on robust psychological research and theory. Progress was also made towards enhancing Sandia National Laboratories existing cognitive models to support culturally plausible behaviors that are important in representing group interactions. These models were implemented in the modular, interoperable, and commercially supported Umbra{reg_sign} simulation framework.
Human behavior modeling for national security concerns
Abstract not provided.
25 Results