Circular Systems Engineering
- URL: http://arxiv.org/abs/2306.17808v4
- Date: Thu, 18 Apr 2024 19:11:49 GMT
- Title: Circular Systems Engineering
- Authors: Istvan David, Dominik Bork, Gerti Kappel,
- Abstract summary: We introduce the concept of circular systems engineering, a novel paradigm for systems sustainability.
We define two principles to successfully implement it: end-to-end sustainability and bipartite sustainability.
We outline typical organizational evolution patterns that lead to the implementation and adoption of circularity principles.
- Score: 0.40964539027092917
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The perception of the value and propriety of modern engineered systems is changing. In addition to their functional and extra-functional properties, nowadays' systems are also evaluated by their sustainability properties. The next generation of systems will be characterized by an overall elevated sustainability -- including their post-life, driven by efficient value retention mechanisms. Current systems engineering practices fall short of supporting these ambitions and need to be revised appropriately. In this paper, we introduce the concept of circular systems engineering, a novel paradigm for systems sustainability, and define two principles to successfully implement it: end-to-end sustainability and bipartite sustainability. We outline typical organizational evolution patterns that lead to the implementation and adoption of circularity principles, and outline key challenges and research opportunities.
Related papers
- Engineering a sustainable world by enhancing the scope of systems of
systems engineering and mastering dynamics [1.3075370397377077]
We discuss the engi-neering of a sustainable world from a systems of systems (SoS) perspective.
We will discuss how suitable the current state-of-the-art in SoS engi-neering is in order to engineer sustainability.
arXiv Detail & Related papers (2024-01-25T10:06:11Z) - Towards a General Framework for Continual Learning with Pre-training [55.88910947643436]
We present a general framework for continual learning of sequentially arrived tasks with the use of pre-training.
We decompose its objective into three hierarchical components, including within-task prediction, task-identity inference, and task-adaptive prediction.
We propose an innovative approach to explicitly optimize these components with parameter-efficient fine-tuning (PEFT) techniques and representation statistics.
arXiv Detail & Related papers (2023-10-21T02:03:38Z) - Future Vision of Dynamic Certification Schemes for Autonomous Systems [3.151005833357807]
We identify several issues with the current certification strategies that could pose serious safety risks.
We highlight the inadequate reflection of software changes in constantly evolving systems and the lack of support for systems' cooperation.
Other shortcomings include the narrow focus of awarded certification, neglecting aspects such as the ethical behavior of autonomous software systems.
arXiv Detail & Related papers (2023-08-20T19:06:57Z) - A Comprehensive Survey of Continual Learning: Theory, Method and
Application [64.23253420555989]
We present a comprehensive survey of continual learning, seeking to bridge the basic settings, theoretical foundations, representative methods, and practical applications.
We summarize the general objectives of continual learning as ensuring a proper stability-plasticity trade-off and an adequate intra/inter-task generalizability in the context of resource efficiency.
arXiv Detail & Related papers (2023-01-31T11:34:56Z) - A Domain-Agnostic Approach for Characterization of Lifelong Learning
Systems [128.63953314853327]
"Lifelong Learning" systems are capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3) Scalability.
We show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems.
arXiv Detail & Related papers (2023-01-18T21:58:54Z) - Systems Challenges for Trustworthy Embodied Systems [0.0]
A new generation of increasingly autonomous and self-learning systems, which we call embodied systems, is about to be developed.
It is crucial to coordinate the behavior of embodied systems in a beneficial manner, ensure their compatibility with our human-centered social values, and design verifiably safe and reliable human-machine interaction.
We are arguing that raditional systems engineering is coming to a climacteric from embedded to embodied systems, and with assuring the trustworthiness of dynamic federations of situationally aware, intent-driven, explorative, ever-evolving, largely non-predictable, and increasingly autonomous embodied systems in
arXiv Detail & Related papers (2022-01-10T15:52:17Z) - Structure-Preserving Learning Using Gaussian Processes and Variational
Integrators [62.31425348954686]
We propose the combination of a variational integrator for the nominal dynamics of a mechanical system and learning residual dynamics with Gaussian process regression.
We extend our approach to systems with known kinematic constraints and provide formal bounds on the prediction uncertainty.
arXiv Detail & Related papers (2021-12-10T11:09:29Z) - Supervised DKRC with Images for Offline System Identification [77.34726150561087]
Modern dynamical systems are becoming increasingly non-linear and complex.
There is a need for a framework to model these systems in a compact and comprehensive representation for prediction and control.
Our approach learns these basis functions using a supervised learning approach.
arXiv Detail & Related papers (2021-09-06T04:39:06Z) - Finance 4.0: Design principles for a value-sensitive cryptoecnomic
system to address sustainability [0.0]
This paper proposes a design science research methodology with value-sensitive design methods to derive design principles for a value-sensitive socio-ecological cryptoeconomic system.
Design principles are implemented in a software that is validated in user studies that demonstrate its relevance, usability and impact.
arXiv Detail & Related papers (2021-05-25T14:09:50Z) - Learning Stable Deep Dynamics Models [91.90131512825504]
We propose an approach for learning dynamical systems that are guaranteed to be stable over the entire state space.
We show that such learning systems are able to model simple dynamical systems and can be combined with additional deep generative models to learn complex dynamics.
arXiv Detail & Related papers (2020-01-17T00:04:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.