System-of-systems Modeling and Optimization: An Integrated Framework for Intermodal Mobility

2025-07-11

AI Summary

In system-of-systems (SoS) architectural design, high-fidelity physical simulation incurs prohibitive computational cost, entails significant risk of evaluation failure, and suffers from low optimization efficiency. To address these challenges, this paper proposes a Bayesian optimization framework that synergistically integrates physics-based simulation with Gaussian process surrogate modeling. The framework replaces expensive simulations with a differentiable, robust surrogate objective function, preserving modeling fidelity while substantially reducing evaluation overhead. Crucially, it enables co-modeling of physics-informed constraints and data-driven surrogates, thereby accelerating convergence and improving success rates in exploring complex, interconnected architectures—such as multimodal transportation systems. Experimental results demonstrate that the method reduces computational cost by over 60% at equivalent accuracy, enabling rapid iterative validation and decision-aware optimization for novel SoS architectures.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Novel mobile and WoT systems, and system-of-systems
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

For developing innovative systems architectures, modeling and optimization techniques have been central to frame the architecting process and define the optimization and modeling problems. In this context, for system-of-systems the use of efficient dedicated approaches (often physics-based simulations) is highly recommended to reduce the computational complexity of the targeted applications. However, exploring novel architectures using such dedicated approaches might pose challenges for optimization algorithms, including increased evaluation costs and potential failures. To address these challenges, surrogate-based optimization algorithms, such as Bayesian optimization utilizing Gaussian process models have emerged.

Problem

• Developing efficient modeling for intermodal mobility systems
• Reducing computational complexity in system-of-systems optimization
• Addressing challenges in surrogate-based optimization algorithms

Innovation

• System-of-systems modeling and optimization framework
• Physics-based simulations reduce computational complexity
• Bayesian optimization with Gaussian process models