Mechanistic Model Parameter Inference With VAE-GAN Mapping
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Solution Overview
Problem
Mechanistic models face challenges in parameter uncertainty and variability in observational data, making it difficult to calibrate them effectively for decision-making applications.
Innovation Solution
Integration of artificial intelligence algorithms, specifically variational autoencoders (VAEs) and generative adversarial networks (GANs), to infer mechanistic model parameters and generate parameter distributions that are coherent with the model's parameter space, addressing stochastic inverse problems and improving calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional calibration methods are used for mechanistic models, then model parameters can be estimated, but parameter uncertainty and variability in observational data make calibration difficult and unreliable
Solution Approach 1:
The patent introduces an intermediary mapping network that learns the transformation from observational data distributions to mechanistic model parameter distributions. This intermediary component bridges the gap between noisy observational data and model parameters, enabling reliable parameter inference despite data variability and uncertainty.
Solution Approach 2:
The patent transforms the calibration problem from directly estimating parameters to learning a distribution mapping. By changing the parameter estimation approach to a distributional mapping learning approach, the system achieves more robust and reliable parameter inference under uncertain conditions.
2Measurement precision
If complex calibration procedures are implemented to handle parameter uncertainty, then parameter estimation accuracy may improve, but computational complexity and time requirements increase
Solution Approach 1:
The patent performs preliminary training of the mapping network on synthetic data that captures the underlying parameter-distribution relationships. This preliminary action enables the network to learn robust mappings in advance, allowing fast and accurate parameter calibration when applied to real observational data without requiring complex iterative procedures at calibration time.
Solution Approach 2:
The patent uses synthetic copies of the observational data generation process to train the mapping network. By creating and training on synthetic data copies, the system learns from simulated scenarios that mirror real-world conditions, enabling accurate parameter estimation without requiring extensive computational resources during actual calibration.
3Productivity
If direct parameter estimation from observational data is performed, then calibration can be achieved, but parameter uncertainty and data variability lead to inaccurate results
Solution Approach 1:
The mapping network serves as an intermediary that processes observational data distributions and transforms them into parameter distributions. This intermediary structure enables efficient calibration by learning the mapping relationship, while simultaneously improving accuracy by accounting for data variability and uncertainty through distributional transformations.
Data Source
AI summary
Techniques regarding inferring parameters of one or more mechanistic models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a machine learning component that can identify a causal relationship in a mechanistic model via a machine learning architecture that employs a parameter space of the mechanistic model as a learned distribution sampled within a generative adversarial network.