Simulation Artifact Knowledge Graph for Validation Efficiency
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Solution Overview
Problem
Current systems face challenges in managing and fusing diverse simulation artifacts across different stages of product design and testing, particularly in multidisciplinary engineering, where cohesion between components is crucial but difficult to maintain, and validation of simulation results requires significant effort.
Innovation Solution
A computer-implemented method and system that parses simulation workspaces to generate knowledge graphs, storing them in a graph database, allowing for contextual similarity analysis and providing recommendations for modifying simulation artifacts based on their relationships, thereby enhancing the management and validation of simulation models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If simulation artifacts are managed in document format, then storage and retrieval are simple, but validation effort and time increase significantly
Solution Approach 1:
The patent replaces manual document-based artifact management with an automated system using knowledge graphs and machine learning models. The system automatically parses simulation artifacts, extracts parameters, builds knowledge graphs, and performs validation, substituting mechanical manual processes with automated computational processes that reduce validation time while maintaining storage simplicity
Solution Approach 2:
The patent introduces knowledge graphs as an intermediary layer between raw simulation artifacts and validation processes. The knowledge graph structure organizes artifact parameters and relationships in a machine-readable format, serving as a mediator that enables automated validation without requiring manual document review, thus reducing validation effort while preserving straightforward artifact storage
2Adaptability or versatility
If simulation models are modified without understanding component cohesion, then design flexibility increases, but model accuracy and reliability decrease
Solution Approach 1:
The patent implements feedback mechanisms where the knowledge graph continuously tracks relationships between simulation components. When designers modify simulation models, the system provides feedback about cohesion and dependency relationships, allowing designers to maintain flexibility while ensuring modifications preserve model accuracy through informed decision-making based on system feedback
Solution Approach 2:
The patent performs preliminary analysis of component cohesion and dependencies before design modifications are made. By pre-establishing the knowledge graph structure and relationships, the system prepares validation criteria in advance, enabling designers to make flexible modifications while automatically checking against pre-defined accuracy requirements
3Measurement precision
If physical component testing is conducted extensively, then validation accuracy improves, but cost and time consumption increase
Solution Approach 1:
The patent creates digital copies of physical test scenarios through simulation artifacts and knowledge graphs. Instead of conducting extensive physical component testing, the system uses simulated environments that replicate real-world conditions, providing sufficient validation accuracy while dramatically improving testing efficiency by eliminating physical constraints and reducing setup time
Solution Approach 2:
The patent enables validation through parameter-based simulation rather than physical testing. By changing simulation parameters to represent different test conditions, the system achieves validation accuracy without the cost and time of physical testing, allowing rapid exploration of multiple scenarios through parameter adjustment rather than physical reconfiguration
4Loss of information
If diverse simulation artifacts from different disciplines are integrated, then comprehensive product understanding improves, but system complexity increases
Solution Approach 1:
The patent implements a universal knowledge graph structure that can accommodate diverse simulation artifacts from different disciplines through a common framework. The standardized graph schema and ontology enable multi-disciplinary integration without requiring separate systems for each discipline, maintaining information cohesion while managing complexity through a unified approach that handles various artifact types consistently
Data Source
AI summary
A computer system and method for managing simulation artifacts is disclosed herein. The method includes parsing at least one first workspace associated with simulation of at least one first product to generate one or more first simulation artifacts. Further, at least one knowledge graph is generated by mapping the one or more first simulation artifacts corresponding to the at least one first product to a predetermined ontology model. Further, a user's intent to modify at least one second simulation artifact in a second workspace is captured based on interaction of the user with a simulation modelling user interface. The second workspace is associated with simulation of a second product. Further, one or more recommendations for modifying the at least one second simulation artifact are generated, based on one or more properties of a contextually similar first simulation artifact, and displayed on the simulation modelling user interface.