Surrogate Model Training via Randomized Controller Simulation
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Solution Overview
Problem
Existing methods for generating and training surrogates for large-scale dynamical systems are computationally expensive and inefficient, requiring significant resources and time, and are unable to effectively handle real-world controllers with unknown parameters.
Innovation Solution
The system generates a training dataset by simulating multiple randomized controllers within a configured space, allowing the surrogate model to be trained without knowledge of the controller's specifics, and iteratively refines the dataset through validation and resampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing methods are used to generate and train surrogates for large-scale models, then the surrogate can be trained, but the computational resources and training time required are prohibitively expensive
Solution Approach 1:
The system performs preliminary actions by generating a diverse set of randomized controllers before training the surrogate model. These pre-generated controllers create training data that covers a wide range of system behaviors, enabling the surrogate to be trained more efficiently without requiring extensive computational resources during the actual training phase.
Solution Approach 2:
The training process is segmented into multiple stages: first generating randomized controllers, then using those controllers to create training datasets, and finally training the surrogate model on this segmented data. This segmentation allows each component to be optimized independently, reducing the overall computational burden and training time.
2Reliability
If existing methods are used to generate and train surrogates for large-scale models, then the surrogate can be trained, but the computational resources required are prohibitively expensive
Solution Approach 1:
The system uses partial action by generating a limited but strategically diverse set of randomized controllers rather than exhaustively testing all possible controllers. This partial sampling approach provides sufficient training data for accurate surrogate generation while significantly reducing computational resource requirements compared to comprehensive testing methods.
Solution Approach 2:
The system creates copies of the physical system through simulated controllers in a virtual environment. These controller copies generate training data that replicates real system behavior without requiring physical experimentation or exhaustive simulation, thereby reducing computational resources while maintaining surrogate accuracy.
3Adaptability or versatility
If the surrogate is trained without knowledge of the controller, then the surrogate can handle real-world controllers with unknown parameters, but the training data generation becomes more complex
Solution Approach 1:
The randomized controllers are designed with universal functionality to work across different system configurations and controller types. By using controllers that can operate independently of specific system parameters, the system achieves broad adaptability to real-world controllers with unknown parameters while managing data generation complexity through standardized controller designs.
Solution Approach 2:
The system manages complexity by systematically varying controller parameters within defined ranges rather than requiring complex adaptive algorithms. This parameter-based approach allows the generation of diverse training data with different controller characteristics while maintaining manageable complexity through structured parameter sampling.
Data Source
AI summary
Systems and methods for generating a training dataset for training a surrogate are disclosed. The surrogate represents a physical system that receives input from the controller. The training dataset is generated without knowledge of how the controller works. A user configures a space from which to sample controllers, and multiple randomized controllers are generated from the configured space. The multiple randomized controllers are simulated, and the resulting dataset from the simulation is used to train the surrogate. The trained surrogate is validated against known data that is not part of the training dataset. Additional controllers are sampled and further data is generated for training of the surrogate.