Digital Twin Transfer Learning Selection for Low-Data Training
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Solution Overview
Problem
The challenge in building digital twins is the large amount of data required for training machine learning models, with simulators often providing imperfect representations and transfer learning techniques varying in data consumption and model quality, and edge-computing not feasible for all devices due to computational limitations.
Innovation Solution
A mechanism for selecting optimal transfer learning techniques by reusing previous experiences from similar environments, utilizing a TL repository with a reward function to minimize data transfer and training costs, and employing a TL proxy agent and repository to manage digital twin training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If simulators are used to train digital twins, then training data can be obtained without real-world data transfer, but the simulators provide imperfect representations of the real environment causing model quality degradation
Solution Approach 1:
The patent introduces transfer learning as an intermediary technique between simulator training and real-world deployment. The system uses a pre-trained model from the simulator as a starting point, then fine-tunes it with limited real-world data through transfer learning, thereby bridging the gap between simulated and real environments while minimizing data transfer requirements
Solution Approach 2:
The system performs preliminary training in the simulator environment before deployment to real-world conditions. By pre-training models in simulation and then applying transfer learning with minimal real data, the system prepares the model in advance to handle real-world variations, reducing the need for extensive real-world data collection
2Quantity of substance
If different transfer learning techniques are used, then data consumption varies, but the quality of the produced model also varies making selection difficult
Solution Approach 1:
The patent implements a dynamic selection mechanism that adapts the transfer learning technique based on the specific digital twin type and available data. The system evaluates multiple TL techniques and selects the optimal one for each scenario, allowing the data consumption and model quality characteristics to dynamically adjust to the specific requirements
Solution Approach 2:
The system changes parameters of the transfer learning process based on the digital twin characteristics. By adjusting TL parameters such as learning rate, data sampling strategies, and fine-tuning depth according to the specific application domain, the system optimizes both data consumption and model quality for each digital twin type
3Ease of operation
If edge-computing is used for training digital twins, then training can occur locally on physical assets, but not all devices have sufficient computational power or capacity
Solution Approach 1:
The patent segments the training process into two parts: initial model training is performed on powerful servers or clouds, and then the pre-trained model is transferred to edge devices for fine-tuning with local data. This segmentation allows devices with limited computational power to still benefit from digital twin training by only performing the lighter fine-tuning stage locally
Data Source
AI summary
Systems and methods are described for the transfer learning for digital twins. Embodiments can include a variety of machine learning, reinforcement learning, transfer learning, and other embodiments. Training digital twins of physical objects can run into the problem of limited access to data transfers. In embodiments under the present disclosure a first training can be performed to determine a source domain or transfer learning method that is best, given a certain state or other metadata of a digital twin. Further training can utilize source domains that previously performed best given state and/or metadata and digital twin type.