17852024. METHOD AND APPARATUS FOR GENERATING PROCESS SIMULATION MODELS simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)
Contents
METHOD AND APPARATUS FOR GENERATING PROCESS SIMULATION MODELS
Organization Name
Inventor(s)
SANGHOON Myung of GOYANG-SI (KR)
HYOWON Moon of HWASEONG-SI (KR)
CHANGWOOK Jeong of HWASEONG-SI (KR)
METHOD AND APPARATUS FOR GENERATING PROCESS SIMULATION MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17852024 titled 'METHOD AND APPARATUS FOR GENERATING PROCESS SIMULATION MODELS
Simplified Explanation
The patent application describes a method for generating a simulation model using simulation data and measurement data of a target. Here are the key points:
- The method involves classifying weight parameters from a pre-learning model into two groups based on their significance.
- The first weight group is retrained using simulation data, while the second weight group is trained using measurement data.
- A transfer learning model is created, which includes the retrained first weight group from the pre-learning model.
- The transfer learning model combines the knowledge gained from simulation data and measurement data to generate an improved simulation model.
Potential applications of this technology:
- This method can be used in various fields that require simulation models, such as engineering, manufacturing, and scientific research.
- It can help in optimizing processes, predicting outcomes, and improving the efficiency of systems.
Problems solved by this technology:
- Traditional simulation models may not accurately represent real-world scenarios due to limitations in data availability or model complexity.
- This method addresses this issue by incorporating both simulation data and measurement data, resulting in a more accurate and reliable simulation model.
Benefits of this technology:
- By retraining the pre-learning model and incorporating measurement data, the generated simulation model is expected to provide more accurate predictions and insights.
- The transfer learning approach allows for leveraging existing knowledge from the pre-learning model, reducing the need for extensive retraining and saving computational resources.
Original Abstract Submitted
A method of generating a simulation model based on simulation data and measurement data of a target includes classifying weight parameters, included in a pre-learning model learned based on the simulation data, as a first weight group and a second weight group based on a degree of significance, retraining the first weight group of the pre-learning model based on the simulation data, and training the second weight group of a transfer learning model based on the measurement data, wherein the transfer learning model includes the first weight group of the pre-learning model retrained based on the simulation data.
