Neural Network Uncertainty Quantification for Evolution Model Parameters
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Solution Overview
Problem
Traditional models for uncertainty quantification in parameter estimates for evolution models are often crafted using the prescribed evolution model, which may not accurately capture the uncertainty in real-world sequential data.
Innovation Solution
A method that involves constructing a loss function for a neural network, incorporating training data values, an evolution model, a noise model, and a prior model, to estimate parameters-of-interest and quantify uncertainty, using a recurrent neural network for sequential data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional models are used for uncertainty quantification in parameter estimates, then the model structure is simple, but the accuracy of uncertainty quantification deteriorates because they cannot accurately capture uncertainty in real-world sequential data
Solution Approach 1:
The patent replaces traditional analytical uncertainty quantification methods with a neural network-based approach. The neural network is trained to predict both parameter estimates and their uncertainties, substituting the mechanical/mathematical traditional modeling approach with an data-driven intelligent system that can capture complex patterns in sequential data.
Solution Approach 2:
The patent transforms the uncertainty quantification problem from a deterministic calculation into a probabilistic prediction task. By training the neural network to output both mean estimates and uncertainty measures (variance), the system adapts traditional parameter estimation to include uncertainty as an additional predicted parameter, improving measurement precision.
2Measurement precision
If a neural network is trained with a comprehensive loss function incorporating multiple models, then the parameter estimation accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-training the neural network offline with a comprehensive loss function that incorporates evolution models, noise models, and prior information. This offline training phase computes the optimal weights once, and then the trained network can be deployed for rapid online inference, reducing real-time computational power requirements while maintaining high accuracy.
Solution Approach 2:
The patent merges multiple models (evolution model, noise model, prior model) into a single unified neural network framework. By combining these separate modeling components into one integrated system with a joint loss function, the patent achieves improved parameter estimation accuracy while avoiding the need to separately compute and coordinate multiple independent models during inference.
Data Source
AI summary
In some embodiments of the invention, a method for estimating parameters of an evolution model includes identifying an evolution model; obtaining a set of training data values, where each value in the set is associated with a parameter-of-interest (PoI) associated with the evolution model; obtaining a noise model representing noise affecting the output of the evolution model; obtaining a prior model that represents prior information on characteristics of the parameter-of-interest; constructing a loss function for a neural network, where the loss function incorporates the set of training data values, the evolution model, the noise model, and the prior model; and training the neural network with the loss function to obtain updated weights.