3D Multi-Scale Modeling via Machine Learning Bridge
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Solution Overview
Problem
Existing multi-scale modeling approaches fail to accurately represent real-world materials and systems, particularly in bridging data between different scales, leading to non-ideal models with limited transferability and extensibility, which hinders the prediction of material performance and behavior across various scales.
Innovation Solution
A computer-implemented method and system for generating a 3D multi-scale model using machine learning to map atomistic materials scale properties to measured properties, enabling the simulation of material performance at bulk and nanometer scales, and incorporating non-local and non-equilibrium phenomena, such as chemical degradation, by creating a chemically and physically realistic model that bridges different scales through the use of deep learning, adversarial learning, or other advanced modeling techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If empirical fits and statistical process models are used to bridge scales, then existing multi-scale modeling can be implemented, but the models lack accuracy in representing real-world materials and have limited transferability and extensibility
Solution Approach 1:
The patent replaces traditional empirical fitting methods and statistical process models with machine learning algorithms. Specifically, it uses supervised learning to train models that map atomistic-scale properties to macro-scale material properties, and unsupervised learning to discover latent structures in multi-scale data. This substitution enables more accurate representation of real-world materials while automatically handling the complexity of multi-scale bridging through data-driven approaches rather than manual empirical relationships.
Solution Approach 2:
The patent transforms the modeling approach by changing from fixed empirical parameters to adaptive machine learning parameters. The system uses training datasets to learn optimal parameter mappings between scales, allowing the model to adapt to different material systems and conditions. This parameter transformation enables the model to maintain high accuracy across diverse applications while the learning process automatically manages the underlying complexity.
2Adaptability or versatility
If multiple models at different scales are used to describe a system, then scale bridging can be achieved, but the process becomes complex and difficult to manage
Solution Approach 1:
The patent merges multiple scale-specific models into a unified multi-scale modeling framework. It integrates atomistic-scale, meso-scale, and macro-scale models through a common machine learning infrastructure that automatically coordinates information flow between scales. This merging maintains the unique capabilities of each scale while presenting a streamlined interface that manages complexity through automated model integration rather than manual coordination.
Solution Approach 2:
The patent creates a universal machine learning platform that handles multiple scaling relationships simultaneously. The same framework can bridge from atomistic to meso-scale, from meso- to macro-scale, or directly from atomistic to macro-scale depending on the application needs. This multi-functional approach enables the system to adapt to various multi-scale problems without requiring separate specialized procedures for each scale transition.
3Measurement precision
If traditional modeling approaches are used, then implementation is straightforward, but the models cannot accurately predict material performance and behavior across various scales
Solution Approach 1:
The patent implements self-service through automated model generation and validation processes. The machine learning framework automatically trains models using provided training datasets, performs cross-validation to assess prediction accuracy, and generates optimized multi-scale models without manual intervention. This automation handles the complex tasks of parameter optimization, model selection, and performance verification, enabling high prediction accuracy while reducing the need for expert manual tuning and validation.
Data Source
AI summary
A computer-implemented method and corresponding computer-based system generate a three-dimensional (3D) multi-scale model of a 3D system. The computer-implemented method generates, at a given scale, an artifact model that indicates properties, characteristics, and artifacts of the 3D system. The computer-implemented method modifies a series of representational models of the 3D system based on the artifact model generated. Modifying the series includes mapping the properties, characteristics, and artifacts to a representational model in the series of representational models at a higher scale or lower scale relative to the given scale. The mapping bridges a given representational model of the series of representational models at the given scale and the representational model at the higher scale or lower scale. The computer-implemented method automatically stores, in a database, the artifact model in association with the series of representational models modified, thereby generating the 3D multi-scale model of the 3D system.