Machine Learning Simulation Parameter Sampling
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Solution Overview
Problem
It is challenging to obtain sufficient observed data for complex models in machine learning, making it difficult to determine optimal simulation parameters that match observed data.
Innovation Solution
An information processing device and method that performs machine learning to model the relation between observed data and simulation parameters, using sampling and simulation calculations to iteratively refine simulation parameters based on error evaluations, enabling accurate prediction even with limited data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning is performed using traditional methods with limited observed data, then the machine learning process becomes insufficient for complex models, but increasing the amount of observed data is difficult
Solution Approach 1:
The patent introduces simulation data as an intermediary between limited observed data and the machine learning model. By generating synthetic simulation data that complements real observed data, the system creates an augmented training dataset without requiring additional physical measurements or observations
Solution Approach 2:
The system performs preliminary simulation calculations before the actual machine learning training to generate training data. By pre-computing simulation results with various parameters, the system prepares a comprehensive dataset in advance that would otherwise require extensive time-consuming observations
2Measurement precision
If more simulation parameters are tested to improve accuracy, then the machine learning accuracy improves, but the computational time and resources increase
Solution Approach 1:
The system implements feedback loops where simulation results are evaluated against observed data, and the machine learning model is iteratively refined. This feedback mechanism allows the system to converge to accurate parameters more efficiently by learning from errors rather than exhaustively searching all parameter combinations
Solution Approach 2:
The system dynamically adjusts simulation parameters during the machine learning process based on performance feedback. By changing parameters adaptively rather than testing all combinations, the system achieves accurate results with fewer computational iterations
Data Source
AI summary
An information processing device 1B mainly includes a machine learning means 15B, a sampling calculation means 16B, and a simulation calculation means 17B. The machine learning means 15B is configured to perform a machine learning of a model which represents the relation among observed data, an observation result, and a simulation parameter, the simulation parameter being required when performing a simulation for predicting the observation result based on observed data. The sampling calculation means 16B is configured to perform, based on a result of the machine learning, sampling of a simulation parameter to be used for machine learning. The simulation calculation means 17B is configured to perform a simulation using the sampled simulation parameter. The machine learning means 15B is configured to perform the machine learning again based on an error evaluation on a simulation result which is a result of the simulation.