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Introduction to Modern Dynamics: Chaos, Networks, Space and Time
No abstract prepared.
Synchronization of Nonlinear Circuits in Dynamic Electrical Networks With General Topologies
Abstract not provided
Agent-Based Modeling in Electrical Energy Markets Using Dynamic Bayesian Networks
Not provided.
Open dynamic simulation dataset of the Texas 2000-bus synthetic power system on ANDES [SWR-25-110]
An open-source power system model (Texas2k), running on an open-source dynamic simulator (ANDES), designed to lower barriers for machine-learning studies of power systems.
Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration
Not provided.
Elastic Optical Networking by Dynamic Optical Arbitrary Waveform Generation and Measurement
Not Available
INDDGO: Integrated Network Decomposition & Dynamic programming for Graph Optimization
It is well-known that dynamic programming algorithms can utilize tree decompositions to provide a way to solve some NP -hard problems on graphs where the complexity is polynomial in the number of nodes and edges in the graph, but exponential in the width of the underlying tree decomposition. However, there has been relatively little computational work done to determine the practical utility of such dynamic programming algorithms. We have developed software to construct tree decompositions using various heuristics and have created a fast, memory-efficient dynamic programming implementation for solving maximum weighted independent set. We describe our software and the algorithms we have implemented, focusing on memory saving techniques for the dynamic programming. We compare the running time and memory usage of our implementation with other techniques for solving maximum weighted independent set, including a commercial integer programming solver and a semi-definite programming solver. Our results indicate that it is possible to solve some instances where the underlying decomposition has width much larger than suggested by the literature. For certain types of problems, our dynamic programming code runs several times faster than these other methods.
RECOVERY ACT - Robust Optimization for Connectivity and Flows in Dynamic Complex Networks
The goal of this project was to study robust connectivity and flow patterns of complex multi-scale systems modeled as networks. Networks provide effective ways to study global, system level properties, as well as local, multi-scale interactions at a component level. Numerous applications from power systems, telecommunication, transportation, biology, social science, and other areas have benefited from novel network-based models and their analysis. Modeling and optimization techniques that employ appropriate measures of risk for identifying robust clusters and resilient network designs in networks subject to uncertain failures were investigated in this collaborative multi-university project. In many practical situations one has to deal with uncertainties associated with possible failures of network components, thereby affecting the overall efficiency and performance of the system (e.g., every node/connection has a probability of partial or complete failure). Some extreme examples include power grid component failures, airline hub failures due to weather, or freeway closures due to emergencies. These are also situations in which people, materials, or other resources need to be managed efficiently. Important practical examples include rerouting flow through power grids, adjusting flight plans, and identifying routes for emergency services and supplies, in the event network elements fail unexpectedly. Solutions that are robust under uncertainty, in addition to being economically efficient, are needed. This project has led to the development of novel models and methodologies that can tackle the optimization problems arising in such situations. A number of new concepts, which have not been previously applied in this setting, were investigated in the framework of the project. The results can potentially help decision-makers to better control and identify robust or risk-averse decisions in such situations. Formulations and optimal solutions of the considered problems need to capture uncertainty and risk using appropriate probabilistic, statistical and optimization concepts. The main difficulty arising in addressing these issues is the dramatic increase in the computational complexity of the resulting optimization problems. This project studied novel models and methodologies for risk-averse network optimization- specifically, network design, network flows and cluster detection problems under uncertainty. The approach taken was to incorporate a quantitative risk measure known as conditional value-at-risk that is widely used in financial applications. This approach presents a viable alternate modeling and optimization framework to chance-constrained optimization and mean-variance optimization, one that also facilitates the detection of risk-averse solutions.
A New Remote Communications Link to Reduce Residential PV Solar Costs
Monitoring of PV/DER site production is expensive to install and unreliable. Among third party systems providers, lost communications links are a growing concern. Nearly 20% of links are failing, provisioning is complex, recovery is expensive, production data is lost, and access is fragmented. FleetLink is a new concept in DER system communications, purpose built for lowering the cost of maintaining active contact with residential end user sites and ensuring that production data is reliably available to third party systems providers. Systems providers require accurate, secure system monitoring and reporting of production data and system faults while driving down overall costs to compete effectively. This plug and play, independently operating communications solution lowers the cost of fleet contact from typically .08 dollars-$.12/W down to .02 dollars -.03/W including installation and maintenance expenses. FleetLink establishes a breakthrough in simplicity that facilitates rapid expansion of residential solar by reducing initial capital outlay and lowering installation labor time and skill levels. The solution also facilitates higher DER installation growth rates by driving down maintenance costs and eliminating communications trouble calls. This is accomplished by the FleetLink’s unique network technology that enables dynamic network configuration for fast changes, and active, self-healing DER site contact for uptime assurance. Using an open source network framework with proprietary, application specific enhancements, FleetLink independently manages connectivity, security, recovery, grid control communications, and fleet expansion while presenting a compliant SunSpec interface to the third party operations centers. The net system cost savings of at least .05 dollars/W supports the SunShot cost goals and the flexibility and scalability of the solution accelerates the velocity and ubiquitous adoption of solar.
Dynamic Polymer Networks for On Demand Degradable Adhesives, Scaffolds, and Templates (Full Technical Report)
This feasibility study was motivated by a need for alternative mandrel materials to meet the next generation of inertial confinement fusion (ICF) ablator capsules. In response, a new polymer material based on reversible covalent bonds was proposed, with the objective to design thermosets to withstand elevated temperatures (140-200 degrees Celsius) while also being thermally degradable on-demand via controlled decomposition at temperatures above 200 degrees Celsius. A series of cyclopentadiene (Cp) containing thermosets were designed and synthesized to evaluate curing, reversibility and degradability via controlled retro-Diels Alder reactions using Cp-cycloaddition adducts as crosslinks. Ultimately, the current iteration of materials and strategy pursued in this study was assessed as impractical for meeting the needs and objectives of Lawrence Livermore National Laboratory’s (LLNL) target fabrication program.
Navigating through large display networks in dynamic control applications
Special display navigation challenges in computer-based display systems for monitoring and controlling dynamic processes are reviewed. Particular attention is given to trends in information technology, workspace design, and paradigmatic cognitive functions related to display navigation.
Guiding Lunar Landers: Harnessing Neural Networks for Dynamic Flight Control with Adaptive Inertia and Mass Characteristics
Explore the source record for details and available documents.
Solid State NMR Investigations of Chain Dynamics and Network Order in Model Poly(dimethylsiloxane) Elastomers
This work is at a relatively early stage, however it has been demonstrated that we can reliably probe basic network architectures using the MQ-NMR technique. The initial results are in good agreement with what is known from standard network theory and will serve as a basis for the study of progressively increasing structural complexity in Siloxane network systems.
Applications of Self-Correcting Hybrid Causal-Learning Systems to Opinion Dynamics on Networks.
Abstract not provided.