STO-Activities: (no title)
Activity title:
Machine Learning and Artificial Intelligence for Military Vehicle Design
Activity Reference:
AVT-411
Panel:
AVT
Security Classification:
PUBLIC RELEASE
Status:
Active
Activity type:
RWS
Start date:
2024-01-01T00:00:00Z
Actual End date:
2025-12-31T00:00:00Z
Keywords:
Artificial Intelligence, Digital Engineering Design, Machine Learning, Multidisciplinary Design Optimization, Simulationdriven design
Background:
Military vehicle requirements continue to evolve rapidly in all AVT domains (air, space, sea, ground). To keep pace with the needs of the Alliance, the time and effort to develop next-generation military vehicles and weapon systems need to be significantly reduced, while simultaneously diminishing problems occurring late in the system development. Meeting these needs requires new and advanced capabilities for quick, accurate and thorough assessment of the design and operational spaces and reliable optimisation processes of platforms’ design. AVT recently completed several assessments of this need.
AVT-ET-054 explored the issue of affordable weapons systems and led to the formation of AVT-092, “Qualification by Analysis”, and AVT-093, “Integrated Tools and Processes for Affordable Weapons Systems”. AVT-093 focused on “the integration of tools and processes, not on the description of tools and processes”. AVT-093 also identified needs in multidisciplinary design optimisation (MDO) that could be addressed using the integration of tools and processes in a distributed parallel computing environment, that would enable feedback of information from detail to preliminary and preliminary to conceptual design. AVT-092 recognized that these capabilities described in AVT-093 are necessary to achieve the objective of rapid design and qualification of new vehicles. Both teams observed a gap between the current technology and the desired end state of rapidly developing affordable weapons systems, recognizing that developments in multidisciplinary technologies are key capabilities for closing that gap. Thus, AVT-237 focused on benchmarking the use and benefits of MDO for the development of military systems, and AVT-252 explored optimization of aircraft and ships under uncertainty. More recently, the AVT-331 on “Goal-driven, multi-fidelity approaches for military vehicle system-level design” discussed, developed, and applied methods for accelerating vehicle design by using tools and processes that reflect different levels of fidelity throughout the MDO process.
Recognising the interdisciplinarity inherent in MDO, AVT-331 recommended broadening the discussion in our community via cooperative events. To this aim, AVT-331 co-chairs and members have organised a special session on Multi-Fidelity Methods for Vehicle Applications at the Multidisciplinary Analysis and Optimization Conference within the AIAA Aviation Forum and Exposition in 2020 and, along the same line, the AVT-354 RWS on “Multi-Fidelity Methods for Military Vehicle Design,” held in 2022 in Varna and included in the STO 2022 Highlights. Finally, AVT-MSG-ET-232 on “Machine Learning and Artificial Intelligence for Military Vehicle Design”, a follow-on activity to AVT-331, discussed ML/AI as enablers to even faster and more accurate design processes, laying the ground for a follow-on RTG. The discussion within AVT-MSG-ET-232 also identified a RWS as a beneficial follow-on activity, based on the positive experience of AVT-331 (RTG) and AVT-354 (RWS) mutually benefiting from each other.
Objectives:
The goal of this RWS is to broaden the discussion in the start-up phase of the companion RTG activity, identifying and extending the state of the art (SoA) of ML/AI methods for vehicle design, investigating how ML/AI can enable faster, more accurate, and more reliable vehicle design processes (conceptual/preliminary/detail). Both experts in ML/AI development and in vehicle design will be invited to study methods and applications of ML/AI across the military vehicle space, exchanging information across disciplines, addressing current capabilities and applications as well as needs and gaps.
Topics:
Relevant scientific topics addressed in the RWS are anticipated to cover:
• Supervised, unsupervised, and reinforcement learning for vehicle design;
• ML/AI enabled simulation-driven design, including multidisciplinary design optimization and optimization under uncertainty;
• Physics-informed ML/AI approaches, transfer learning, and approaches for the use of already available experimental and computational data;
• Data sources, types, sample sizes, and reliability for typical design applications and the interdependence of ML/AI and data generation for vehicle design;
• Review of state of the art on ML/AI for military vehicle, comparison of methods, current gaps and limitations, best practices.
Contact:
Open2Partners:
Title:
Created at 24/10/2023 12:00 by System Account
Last modified at 16/05/2024 09:00 by System Account
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