Data-Driven Characterization of Transport Properties in Heterogeneous Materials
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Solution Overview
Problem
Current material analysis techniques are costly and operationally challenging, particularly in characterizing the spatial distribution of transport properties in heterogeneous media, often requiring extensive infrastructure and human involvement, which can lead to biased and inaccurate results.
Innovation Solution
A data-driven and intelligent characterization method using machine-learning algorithms and computational methods, implemented through a system comprising a receiver, designer, modifier, evaluator, and procedure learner, that processes temporal measurements of energy, mass, and momentum transport, and gradients to iteratively refine the spatial distribution of transport properties in heterogeneous materials, reducing human intervention and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional material analysis techniques are used to characterize spatial distribution of transport properties, then measurement precision may be maintained, but device complexity and operational challenges increase significantly
Solution Approach 1:
The patent replaces complex mechanical measurement infrastructure with a computational system that uses machine learning algorithms to characterize transport properties. Instead of using elaborate physical measurement devices, the system employs computational models trained on measurement data to infer spatial distributions of transport properties, thereby reducing device complexity while maintaining characterization accuracy.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between raw measurement data and the final characterization results. This intermediary computational layer processes measurement data to infer transport properties, eliminating the need for complex direct measurement infrastructure and reducing operational challenges.
2Measurement precision
If traditional material analysis techniques are used, then comprehensive material characterization can be achieved, but loss of time and operational efficiency worsen
Solution Approach 1:
The patent performs preliminary action by training machine learning models on measurement data in advance. Once trained, these models can rapidly characterize new materials without requiring time-consuming traditional analysis procedures for each new sample, significantly reducing analysis time while maintaining comprehensive characterization.
Solution Approach 2:
The patent enables continuous characterization by using machine learning models that can process measurement data rapidly and continuously. Unlike traditional methods that require repeated manual analysis steps, the computational model provides continuous inference capability, reducing overall analysis time while maintaining characterization completeness.
3Measurement precision
If human involvement is increased in material analysis, then measurement precision may be maintained through expert judgment, but ease of operation and automation level decrease
Solution Approach 1:
The patent implements self-service by designing a machine learning system that automatically performs material characterization without requiring human expertise for each analysis. The model trains on measurement data and then autonomously infers transport properties, eliminating the need for human experts to manually analyze each sample while maintaining high accuracy through the learned patterns.
Solution Approach 2:
The patent incorporates feedback mechanisms where the machine learning model learns from measurement data and continuously improves its characterization accuracy. The system uses feedback from training data to refine its predictions, achieving high precision automatically without requiring human judgment for each measurement while maintaining ease of operation.
4Reliability
If conventional analysis methods are used, then established procedures can be followed, but productivity and cost-effectiveness worsen
Solution Approach 1:
The patent changes the fundamental parameters of the analysis method by transitioning from manual or semi-automated conventional procedures to a fully computational machine learning approach. This parameter change enables parallel processing of multiple samples and rapid inference, dramatically increasing productivity and cost-effectiveness while maintaining reliability through the established machine learning framework.
Data Source
AI summary
A method comprises obtaining temporal measurements associated with a heterogeneous material; building a numerical model of a material by assigning initial approximations to the temporal measurements; modifying the numerical model to create a modified numerical model; generating simulated temporal measurements associated with the temporal measurements using the modified numerical model; determining a reward, a penalty, or a modification based on a quality of a fit between the temporal measurements and the simulated temporal measurements; and updating the numerical model based on the reward, the penalty, or the modification.