TL;DR
MIT researchers have created a modular design framework that folds uncertainty about individual components into the co-design of complex systems. The approach models probabilities of many outcomes, not just best- and worst-case scenarios, and was demonstrated on drone battery and perception tradeoffs.
What happened
A team at MIT developed a new co-design framework that explicitly incorporates uncertainty in the performance of individual components when engineering complex systems. Building on the lab’s co-design language, the researchers used mathematical reformulation and tools from category theory to make it possible to combine modular component models while preserving relationships among uncertain parameters. The system is plug-and-play: engineers can rearrange component blocks without breaking the mathematical structure, and multidisciplinary teams can contribute component models without deep expertise in the full system. The team applied the framework to selecting perception systems and batteries for a delivery drone, revealing probabilistic tradeoffs — for example, a battery design showed a 12.8% chance of infeasibility at a 1,750-gram payload. Authors include Gioele Zardini (senior author), lead author Yujun Huang, and Marius Furter; the work was presented at the IEEE Conference on Decision and Control.
Why it matters
- Designers can assess a range of likely outcomes instead of only best- and worst-case scenarios, improving risk-aware decisions.
- Modularity and reduced need for domain-specific tooling make it easier for multidisciplinary teams to co-design large systems.
- Applicable to high-stakes systems — from drones to autonomous vehicles and transportation networks — where component behavior is unpredictable.
- Provides finer-grained tradeoffs (for example, between cost, weight and payload) that can change recommended component choices.
Key facts
- The framework extends the group’s existing co-design language to include parametric and composable uncertainty.
- Researchers used category-theory–based reformulation to preserve relationships among components while simplifying computation.
- The system is modular: components are represented as interchangeable 'boxes' that can be reconfigured without violating mathematical constraints.
- Applied example: selecting perception systems and batteries for a delivery drone to maximize payload while minimizing lifetime cost and weight.
- The framework produces probabilistic outcomes and tradeoffs rather than only best- and worst-case bounds.
- A reported result showed a battery design had a 12.8% probability of being infeasible at a 1,750-gram drone payload.
- Authors named in the paper are Gioele Zardini (senior author), Yujun Huang (lead author), and Marius Furter; the paper was presented at the IEEE Conference on Decision and Control.
- Future work cited by the team includes improving computational efficiency and extending the approach to systems designed by multiple, potentially competing parties.
What to watch next
- Improvements to the computational algorithms used to solve the reformulated co-design problems (future work identified by the authors).
- Efforts to extend the framework to multi-party design contexts where components are developed by collaborative or competing organizations.
- Potential translation of the approach into design processes for autonomous vehicles, commercial aircraft, or regional transportation networks.
Quick glossary
- Co-design: A design approach that treats a system as interconnected components and optimizes them jointly rather than separately.
- Category theory: An abstract mathematical framework for describing and composing structures and relationships; used here to maintain composability of component models.
- Parametric uncertainty: Uncertainty in the numerical parameters that describe a component’s behavior or performance specifications.
- Feasibility (design): Whether a proposed configuration meets required constraints and can operate as intended under modeled conditions.
Reader FAQ
Who led the research?
Gioele Zardini is the senior author; lead author is Yujun Huang, with co-author Marius Furter. The work comes from MIT-affiliated researchers.
Has the framework been tested on real hardware?
not confirmed in the source
Does the method require deep domain expertise to use?
The researchers say the system reduces the need for extensive domain expertise by using modular, plug-and-play component models.
Where was the work presented?
The paper was presented at the IEEE Conference on Decision and Control.
The approach could enable autonomous vehicles, commercial aircraft, or transportation networks that are more reliable in the face of real-world unpredictability. Adam Zewe | MIT News Publication Date : October…
Sources
- Accounting for uncertainty to help engineers design complex systems
- MIT Innovates New Framework to Enhance Engineering of …
- A New Approach for Engineers in Designing Complex
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