Systems engineering is undergoing a transformation motivated by mission and system complexity, and enabled by technological advances such as model-based systems engineering, digital engineering, formal modeling, and the convergence of systems engineering with other disciplines. This conference is focused on exploring recent trends and advances in model-based systems engineering (MBSE) and the synergy of MBSE with simulation technology and digital engineering. Researchers have submitted papers on MBSE methods, modeling approaches, standards, languages, and economics analysis to respond to the challenges posed by 21st century systems.
The Institute of Electrical and Electronics Engineers (IEEE) Systems Council facilitates interactions among communities of interest on system-level problems and applications. System-level thinking is essential in the world today, not only for technical systems, but also for society at large. The Council addresses the discipline of systems engineering, including theory, technology, methodology, and applications of complex systems, system-of-systems, and integrated systems of national and global significance.
- Robotic Systems
- Sensors Integration & Application
- Large-Scale Systems Integration
- Space and Communication Systems
- Medical Systems
- Defense Systems
- Gaming and Entertainment Systems
- Transportation Systems
- Environmental Systems
- Energy Management and Sustainability, including Renewable Energy
- Autonomous Systems
- Systems Engineering
- Engineering Systems-of-Systems
- Systems Architecture
- Complex Systems
- Cyber Security
- Cloud Computing
- Modeling & Simulation
- Model-Based Systems Engineering
- Systems Engineering Education & Theory
- Systems Integration & Verification
- Decision-making for Complex Systems
Speaker: Dr. Judith Dahmann, Technical Fellow, The MITRE Corporation | CONTACT
If you have any questions regarding the logistics for the Talk series, please contact our Talks webinar coordinator, Ms. Mimi Marcus. If you would like to present in a future SERC Talk, please contact our SERC Talks Editor-in-Chief and SERC Research Council Chair, Dr. Barry Boehm. More information on future SERC Talks can be found here. Thank you!
NOTE TO PARTICIPANTS:
SERC Talks broadcasts using the Zoom Webinar Platform. Prior to the SERC Talk, we encourage all participants to install and test the platform to exercise full virtual capabilities. Installation is NOT required to join the session, as Zoom is accessible via web browser.
More information on future SERC Talks can be found here. Thank you!
The Digital Readiness Webcast Series is a collaboration between the Defense Acquisition University and the System Engineering Research Center (SERC). Each webcast will be headlined by various SERC researchers throughout our network of collaborators.
This 9-part webcast series will be centered around the following themes:
ABSTRACT: The digital transformation is fundamentally changing the state of practice across a wide range of government agencies, industries and academia. As the DoD transitions to digital engineering, there is a need to develop and maintain an acquisition workforce and culture that is competent in digital engineering practices, tools, and outputs across the acquisition lifecycle. DoD defines digital engineering as an integrated digital approach that uses authoritative sources of system data and models as a continuum across disciplines to support lifecycle activities from concept through disposal. Digital engineering impacts how engineers perform their job functions, the tools required, the products delivered, and the interaction and sharing of data either locally or distributed. In order to succeed in digital engineering, deliberate efforts to develop new competencies for education and training of the DoD workforce must be implemented. This presentation describes digital engineering, its potential impact, the necessary methodological transition and the skill sets that will be essential for its support.
ABSTRACT: This presentation will outline some of the fundamental drivers for Digital Transformation, why Digital Engineering is the key to the broader Digital Transformation, as well as share some best practices, lessons learned, and activities underway to help pave the path forward for Digital Engineering.
ABSTRACT: This Webcast provides an overview of the NAVAIR Systems Engineering Transformation (SET) Surrogate Pilot Experiments and discusses how these experiments provide implementation examples that align with the goals of the DoD Digital Engineering Strategy. It provides an overview to set the context of the SET Framework concept and Functional Areas.
The Webcast discusses enabling technologies for Digital Engineering examples that support the objectives of the SE Transformation. The Webcast starts with our efforts to formalize the artifacts for the NAVAIR Systems Engineering (NAVSEM) method using View and Viewpoints for automatically generating a “specification” directly from the model using DocGen.
The Webcast will also show the models that are on line and provide information on the All Partners Network (APAN.org) where additional details about approach, models, presentations and results can be accessed.
Data Analytics is the science of analyzing data to convert information to useful knowledge. This knowledge could help us understand our world better, and in many contexts enable us to make better decisions. To make this goal more attainable is the steep decrease of costs to gather, store, and process data, along with a growing motivation for the use of empirical approaches to problem solving.
Data Science – that is the overall umbrella where Data Analytics are studied and applied – is the study of the generalizable extraction of knowledge from data. Being a data scientist requires an integrated skill set spanning mathematics, statistics, machine learning, databases and other branches of computer science along with a good understanding of the craft of problem formulation to engineer effective solutions.
This presentation describes Data Analytics and Data Science, its potential impact, the necessary theoretical and practical elements to implement it and a set of case studies of application.
Data science is becoming an integral part of everything we do each day. Initially an exclusive domain of large corporation, data science is now at the personal level, with applications from domotics to wearables. What is adding value to our activities and what is just subtracting time? What are the technologies and approaches behind the most valuable solutions?
Data is market per se. Companies are investing in harvesting, curate, tag data, generating a huge market, serving sectors from marketing to security to intelligence. How can we benefit from the current increasing data availability and from the synergies between the different sources?
This presentation addresses the above questions, providing a picture of the general state of practice.
Future cannot be predicted, but in science there is a high level of consistency over time. Data Science today is a steppingstone for an even more informed and complex way of living and doing business, with a continuous integration of sources and media, creating semantic synergies, pushing the boundaries of convenience, value and privacy.
This presentation scans the major trends in Data Science, starting from the current emerging trends, extrapolating scenarios.
ARTIFICIAL INTELLIGENCE & MACHINE LEARNING
Artificial Intelligence and Machine Learning (AI/ML) have had a fascinating evolution from 1950 to the present. This talk sketches the main themes of AI and machine learning, tracing the evolution of the field since its beginning in the 1950s and explaining some of its main concepts. These eras are characterized as “from knowledge is power” to “data is king”.
Will there be another AI Winter?
We will explore some clues to where the current AI/ML may reunite with GOFAI (Good Old Fashioned AI) and hopefully expand the utility of both. This will include extrapolating on the necessary melding of AI with engineering, particularly systems engineering.
The Defense Acquisition University (DAU) is the primary training organization for the Defense Acquisition Workforce. DAU is committed to providing the training—both formal and informal—to improve the professionalism of the acquisition workforce by engaging our students both in the classroom and on the job. Our products and services enhance workplace performance, promote mission effectiveness, and help reshape the workforce to meet future challenges—ultimately developing fully qualified acquisition professionals who provide cost-effective systems, equipment, and services to meet Warfighter requirements.
To access the latest OUSD(R&E) Digital Engineering Working Group Newsletter, download pdf below.
ABOUT THE DEWG: To further the digital engineering effort, the DoD Engineering office leads the Digital Engineering Working Group (DEWG) whose participants represent segments of the engineering and acquisition communities including Program Executive Offices, Program Managers, engineering, and science and technology proponents. The DEWG promotes digital engineering principles throughout the Services and other agencies and can assist in advancing digital engineering practices.
Benchmarking Metrics, Benefits and Current Maturity of Model-Based Systems Engineering across the Enterprise
In 2019-2020, the National Defense Industrial Association Systems Engineering Division (NDIA-SED) and the International Council on Systems Engineering (INCOSE) collaborated with the Systems Engineering Research Center (SERC) at the Stevens Institute of Technology to benchmark the current state of Digital Engineering (DE) and Model-Based Systems Engineering (MBSE) across government, industry, and academia. The team developed and executed a survey of the systems engineering community to broadly assess the maturity of system engineering’s “digital transformation,” identify specific benefits of MBSE and associated metrics, identify enablers and obstacles to DE and MBSE adoption across the enterprise, and understand evolving and necessary shifts in the systems engineering (SE) workforce. The survey results have now been used to publish a research report on Digital Engineering Metrics.
UPDATE: The summary and full DE Metrics reports have been added to this page.
The SERC-2020-SR-003 Summary Report on Digital Engineering Metrics focuses in on how organizations can categorize and measure Digital Engineering change. This report was developed from the survey data and additional research on enterprise change measurement. The SERC-2020-TR-002 Digital Engineering Metrics full research report includes all of the background research completed in support of the survey data analysis and the metrics recommendations.
The SERC-2020-SR-001 report: “Benchmarking the Benefits and Current Maturity of Model-Based Systems Engineering across the Enterprise,” indexes the findings drawn from the MBSE Maturity Survey. The survey itself was released by the Systems Engineering Research Center, INCOSE and NDIA during late 2019, and closed on February 1, 2020. Thank you again to all participants.
March 19, 2020 — Benchmarking the Benefits and Current Maturity of Model-Based Systems Engineering across the Enterprise Results of the MBSE Maturity Survey / Part 1: Executive Summary
View the SERC-2020-SR-001 report on the results of the MBSE Maturity Survey
March 26, 2020 — The Quality Measures for MBSE Working Group Meeting – Tom McDermott – Deputy Director, Systems Engineering Research Center – presented the following: “Benchmarking the Benefits and Current Maturity of Digital Engineering/Model-Based SE”
View Recording from Working Group Meeting
Speaker: Dr. Martin Törngren, Professor, KTH Royal Institute of Technology | CONTACT
Trustworthiness in particular means that the complexity of CPS needs to be manageable and that known challenges – such as the automation paradox, newer challenges – such as failure modes of AI and deep learning systems, and expanding challenges – such as those related to cyber-security and safety of open world systems, are understood and properly handled. This talk will provide the following perspectives to human centered CPS.
– trends and capabilities of future CPS, drawing upon lessons learnt from previous technological shifts,
– a complexity analysis of CPS, used to highlight bottlenecks in current (systems) engineering practices,
– directions towards trustworthy and circular CPS, including automated driving and edge computing will be taken as technological case studies for illustrative purposes.
More information on future SERC Talks can be found here. Thank you!
This Talk is free to the public and all are welcomed to join and participate in this open discussion. Slides will be up at least two hours prior to the Talk if not sooner. If you have any questions regarding joining this session, please contact the SERC Talks webinar coordinator, Ms. Mimi Marcus. If you would like to present in a future SERC Talk, please contact our SERC Talks Editor-in-Chief and SERC Research Council Chair, Dr. Barry Boehm. Please register for meeting details.
This message is to inform you that due to the COVID-19 outbreak, we unfortunately have to postpone the Model Curation Workshop scheduled for April 30, 2020. We are currently exploring new dates for Fall 2020.
We sincerely apologize for any inconvenience this may cause and look forward to reconnecting with you, once the workshop date has been rescheduled.
Donna H. Rhodes
Principal Research Scientist
Massachusetts Institute of Technology
SERC Workshop on MODEL CURATION: “Maximizing the Enterprise Benefits of the Investment in Authoritative Source of Truth”
Collections or repositories of models, simulations and data have been used for some time, and the transformation to digital engineering is impacting their scale and importance to programs and enterprises. The significant investment and complexity in establishing authoritative source of truth drives the need for increased governance and enhanced practices. New supporting technologies and enabling infrastructure are needed to achieve enterprise-level benefit.
Model curation provides a strategic, disciplined approach, with governance and enterprise-level practices to understand the complexity, and ensure the investment in the authoritative source of truth provides enduring benefit for current and future engineering programs. This workshop explores model curation across three areas: governance, infrastructure, and the vision for model discovery in support of strategic reuse of the model collection.
• Governance: The strategic value of the enterprise model collection will depend upon strong governance. The workshop will explore topics such as structures for governance authority, model collection valuation and IP management, accreditation and trust/credibility factors.
• Infrastructure: Supporting infrastructure for a model collection repository needs to be scalable and strategically managed to support the current and future needs of the enterprise. The workshop will explore topics such as: security and protection, scalability, enabling model sharing and remote access, etc.
• Model Discovery: Enterprise model repositories, or libraries, are envisioned as comprised of a collection of model assets that are discoverable, retrievable, and reusable. The workshop will explore approaches and technologies that support effective and efficient discovery of suitable models, searchable categorization, augmented search and decision making, etc.
A room block is available at:
Courtyard Washington DC / Dupont Circle
1900 Connecticut Ave. NW
Washington, DC 20009
The March 2020 issue of INSIGHT addresses augmented and artificial intelligence (AI) for systems engineering (AI4SE) and systems engineering for augmented and artificial intelligence (SE4AI). SE4AI addresses the transformation we need in methods, procedures, and tools (MPTs) to engineer systems with embedded AI to be fit for purpose and doing no (unintended) harm. AI4SE addresses challenges that have to be overcome to leverage AI in the practice of systems engineering much as a lever or pulley provides mechanical advantage to perform work in Newtonian mechanics.
Tenets of systems engineering from control systems engineering are that engineered systems be observable, controllable (to assure system stability with tolerable errors), and identifiable (Möller 2016). AI is in the forefront of technology advances with widely publicized innovations in image identification, diagnostics, and autonomy; AI applications gone awry are newsworthy in both the popular and scientific/engineering media.
AI methods can be broadly classed as rule-based and neural-network based. Rule-based methods are relatively mature with decades of experience in application and are well-understood. In contrast, much is unknown of the contextually driven behavioral characteristics of neural network-based AI, commonly referred to as machine learning and deep learning.
Neural network performance is critically dependent on the datasets used to train the algorithms and whose actions currently cannot be guaranteed to be fit for purpose to meet the attributes of elegance that systems accomplish their intended purposes, be resilient to effects in real-world operation, while minimizing unintended actions, side effects, and consequences (Griffin 2010).
The articles below represent the Systems Engineering Research Center’s (SERC) ongoing research in addressing AI4SE and SE4AI challenges as stated in their research roadmap for AI and autonomy (SERC 2019).
NOTE: All articles are available via open access courtesy of INCOSE and Wiley for the entire duration of 2020.
Knowledge Representation with Ontologies and Semantic Web Technologies to Promote Augmented and Artificial Intelligence in Systems Engineering
Thomas Hagedorn, Mary Bone, Benjamin Kruse, Ian Grosse, Mark Blackburn
- Griffin, M. D. 2010. “How do we fix system engineering?” 61st International Astronautical Congress. Prague, CZ; 27 September – 1 October.
- Möller, D. P. F. 2016. Guide to Computing Fundamentals in Cyber-Physical Systems: Concepts, Design Methods, and Applications. Springer, CH.
- Systems Engineering Research Center. 2020. “Research Roadmaps 2019-2020.”