Dynamic State Management in Physical System Networks
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Solution Overview
Problem
Current systems for modeling and simulating physical systems lack effective methods to dynamically manage and visualize the state of complex networks, particularly in managing constraints, targets, and scaffolds, which are crucial for accurately representing and simulating mechanical and electro-mechanical systems.
Innovation Solution
A technical computing environment (TCE) is developed that includes tools for building and executing networks representing systems, allowing for the specification of targets, constraints, and scaffolds, and automatically generating visualizations and code to simulate these systems, enabling dynamic state management and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex networks representing physical systems are modeled and simulated, then the accuracy of system representation is improved, but the device complexity increases
Solution Approach 1:
The patent segments the complex system modeling into distinct functional components: networks represent the system structure, states represent system conditions, constraints define system boundaries, targets specify desired outcomes, and scaffolds provide structural support. This segmentation allows complex physical systems to be modeled accurately while managing complexity through modular organization of modeling elements.
2Measurement precision
If dynamic state management is implemented for complex networks, then the simulation accuracy is improved, but the computational resources and time required increase
Solution Approach 1:
The patent establishes constraints, targets, and scaffolds before executing the simulation. These pre-defined elements prepare the system state management structure in advance, allowing the simulation to proceed efficiently by evaluating against pre-established criteria rather than computing constraints dynamically during simulation execution.
Solution Approach 2:
The system continuously evaluates network states against constraints, targets, and scaffolds, providing feedback that guides state transitions. This feedback mechanism enables accurate dynamic state management by systematically checking state validity and guiding the system toward target states while maintaining constraint satisfaction throughout the simulation.
3Ease of operation
If visualizations and code are automatically generated from network models, then the ease of operation is improved, but the device complexity increases
Solution Approach 1:
The patent automatically generates visualizations and code by creating representations of the network model. The network structure, states, constraints, targets, and scaffolds are copied and transformed into visual formats and executable code, enabling users to deploy simulations without manually writing code while managing the complexity of the generation process through automated template-based approaches.
Data Source
AI summary
In an embodiment, a network may represent a physical system. The network may have an element that represents an entity of the physical system. A value of a state associated with the network may be identified (e.g., generated) using various techniques. The state may be a low-level state associated with the network. The techniques may include, but are not limited to, for example, generating the value based on a scaffold defined for the network, generating the value based on a target value for the state, and/or generating the value based on applying an operation to various values of the state. The identified value may be associated with an identifier. The identifier may distinguish the value, for example, from other values of other states in the network and/or other values of states in other networks.