Data-Oriented Modeling for Spacecraft Design
- URL: http://arxiv.org/abs/2603.24841v1
- Date: Wed, 25 Mar 2026 22:12:51 GMT
- Title: Data-Oriented Modeling for Spacecraft Design
- Abstract summary: This paper presents a data-oriented approach to Model-Based Systems Engineering that adapts the Entity-Component-System architecture from the video game industry.<n>Design data is stored as immutable, format-agnostic components in a generic data system; stateless analysis functions operate on this data through templates and containerized tools.<n>A prototype implementation, VVERDAD, demonstrates the approach on an example interplanetary mission concept.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Spacecraft development costs remain high despite falling launch costs, in part because Model-Based Systems Engineering (MBSE) tools carry the complexity of the object-oriented programming paradigm: tightly coupled data and logic, mutable state, and rigid class hierarchies that resist integration with discipline-specific analysis tools. This paper presents a data-oriented approach to MBSE that adapts the Entity-Component-System (ECS) architecture from the video game industry. Design data is stored as immutable, format-agnostic components in a generic data system; stateless analysis functions operate on this data through templates and containerized tools within a continuous integration pipeline. A prototype implementation, VVERDAD (https://github.com/VisVivaSpace/vverdad-prototype), demonstrates the approach on an example interplanetary mission concept, showing how data-oriented principles can reduce deployment complexity, simplify testing, and preserve the traceability benefits of document-based systems engineering.
Related papers
- IDDMBSE: Integrating Data-Driven and Model-Based Systems Engineering for Trusted Autonomous Cyber-Physical Systems [3.043506365580073]
We address the gap between Model-Based Systems Engineering (MBSE) and data-driven Machine Learning and Artificial Intelligence (ML/AI)<n>We address this gap with IDDMBSE, an Integrated Data-Driven and Model-Based Systems Engineering methodology that extends the rigorous MBSE V-process with a data-driven loop at every step.<n>We demonstrate IDDMBSE on a Trusted Autonomous Ground Robot across its development lifecycle, spanning sensor-suite selection, risk-sensitive path planning, behavior-tree task verification, conformal-prediction-based robust perception, and assured multi-robot coordination.
arXiv Detail & Related papers (2026-06-04T21:22:51Z) - Vision-Language-Action in Robotics: A Survey of Datasets, Benchmarks, and Data Engines [14.989533826340486]
We present a systematic, data-centric analysis of VLA research organized around three pillars: datasets, benchmarks, and data engines.<n>For datasets, we categorize real-world and synthetic corpora along embodiment diversity, modality composition, and action space formulation.<n>For benchmarks, we analyze task complexity and environment structure jointly, exposing structural gaps in compositional generalization.<n>For data engines, we examine simulation-based, video-reconstruction, and automated task-generation paradigms.
arXiv Detail & Related papers (2026-04-24T20:41:59Z) - A Framework and Prototype for a Navigable Map of Datasets in Engineering Design and Systems Engineering [0.0]
This paper proposes a systematic framework for a "Map of Datasets in EDSE"<n>The framework is built upon a multi-dimensional taxonomy designed to classify engineering datasets by domain, lifecycle stage, data type, and format.<n>An analysis of the current data landscape reveals underrepresented areas ("data deserts") in early-stage design and system architecture, as well as relatively well-represented areas ("data oases") in predictive maintenance and autonomous systems.
arXiv Detail & Related papers (2026-03-16T17:08:20Z) - Understanding by Reconstruction: Reversing the Software Development Process for LLM Pretraining [66.89012795621349]
Large Language Models (LLMs) often struggle with the deep, long-horizon reasoning required for complex software engineering.<n>We propose a novel paradigm: understanding via reconstruction.<n>We introduce a framework that synthesizes latent agentic trajectories using a multi-agent simulation.
arXiv Detail & Related papers (2026-03-11T09:23:20Z) - Steerable Instruction Following Coding Data Synthesis with Actor-Parametric Schema Co-Evolution [30.919665619254]
We propose IFCodeEvolve, a framework for synthesizing large-scale instruction-paired coding data.<n>We show that IFCodeEvolve significantly boosts base model performance, with our 32B model achieving parity with proprietary SOTA models.<n>We also contribute IFCodeBench, a comprehensive human-verified benchmark equipped with solutions and robust AST-based verification.
arXiv Detail & Related papers (2026-02-27T21:31:41Z) - GENAI WORKBENCH: AI-Assisted Analysis and Synthesis of Engineering Systems from Multimodal Engineering Data [0.0]
GenAI is a Model-Based Systems Engineering (MBSE) environment that integrates systems engineering principles into the designer's workflow.<n>Built on an open-source PLM platform, it establishes a unified digital thread by linking semantic data from documents, physical B-rep geometry, and relational system graphs.<n>This paper presents the conceptual architecture, proposed methodology, and anticipated impact of this work-in-progress framework.
arXiv Detail & Related papers (2026-02-27T19:04:13Z) - Nimbus: A Unified Embodied Synthetic Data Generation Framework [51.55989844555466]
Scaling data volume and diversity is critical for generalizing embodied intelligence.<n>We present Nimbus, a unified synthetic data generation framework designed to integrate heterogeneous navigation and manipulation pipelines.<n>Our evaluation demonstrates that Nimbus achieves a 2-3X improvement in end-to-end throughput compared to unoptimized baselines.
arXiv Detail & Related papers (2026-01-29T09:27:31Z) - Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey [59.3507264893654]
Issue resolution is a complex Software Engineering task integral to real-world development.<n> benchmarks like SWE-bench revealed this task as profoundly difficult for large language models.<n>This paper presents a systematic survey of this emerging domain.
arXiv Detail & Related papers (2026-01-15T18:55:03Z) - Affordance Representation and Recognition for Autonomous Agents [64.39018305018904]
This paper introduces a pattern language for world modeling from structured data.<n>The DOM Transduction Pattern addresses the challenge of web page complexity.<n>The Hypermedia Affordances Recognition Pattern enables the agent to dynamically enrich its world model.
arXiv Detail & Related papers (2025-10-28T14:27:28Z) - Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments [70.42705564227548]
We propose an automated environment construction pipeline for large language models (LLMs)<n>This enables the creation of high-quality training environments that provide detailed and measurable feedback without relying on external tools.<n>We also introduce a verifiable reward mechanism that evaluates both the precision of tool use and the completeness of task execution.
arXiv Detail & Related papers (2025-08-12T09:45:19Z) - A Serverless Architecture for Real-Time Stock Analysis using Large Language Models: An Iterative Development and Debugging Case Study [0.0]
This paper documents the design, implementation, and iterative debug of a novel, serverless system for real-time stock analysis.<n>We detail the architectural evolution of the system, from initial concepts to a robust, event-driven pipeline.<n>The final architecture operates at a near-zero cost, demonstrating a viable model for individuals to build sophisticated AI-powered financial tools.
arXiv Detail & Related papers (2025-07-13T11:29:51Z) - T-METASET: Task-Aware Generation of Metamaterial Datasets by
Diversity-Based Active Learning [14.668178146934588]
We propose t-METASET: an intelligent data acquisition framework for task-aware dataset generation.
We validate the proposed framework in three hypothetical deployment scenarios, which encompass general use, task-aware use, and tailorable use.
arXiv Detail & Related papers (2022-02-21T22:46:49Z) - SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines.
This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
arXiv Detail & Related papers (2021-12-22T14:45:37Z)
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.