Physics-Informed Neural Networks for Solid Material Defect Analysis
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Solution Overview
Problem
Existing technologies face challenges in efficiently analyzing and designing the internal structures and defects of solid materials using physics-informed machine learning, particularly in accurately characterizing unknown geometries and optimizing material performance with limited non-destructive measurements.
Innovation Solution
The use of physics-informed neural networks (PINNs) to analyze and design solid materials by integrating geometric variables into the networks, allowing for the characterization of internal structures and defects, and the design of optimized geometries through a trainable manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physics-informed neural networks are used to analyze internal structures with limited measurements, then predictive capability is improved, but measurement precision requirements increase
Solution Approach 1:
The patent transforms the measurement precision problem by changing the parameter representation from direct geometric measurements to physics-informed latent variables. The PINN framework incorporates physical laws (PDEs) as constraints, allowing the system to infer internal structures from limited boundary measurements by solving an inverse problem. This parameter transformation enables reliable predictions despite limited input data quality.
Solution Approach 2:
The patent introduces physics-informed neural networks as an intermediary between limited measurements and internal structure characterization. The PINN acts as a mediator that incorporates physical laws (equations of elasticity, heat conduction, etc.) to bridge the information gap, allowing accurate reconstruction of internal geometries from boundary measurements alone without requiring direct internal measurements.
2Manufacturing precision
If geometry identification is performed in a trainable manner using PINNs, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal PINN framework that can identify multiple geometric parameters (internal boundaries, defect locations, material interfaces) simultaneously through a single trainable model. The framework is multi-functional, handling forward propagation, inverse problem solving, and geometry reconstruction within one unified system, reducing the need for multiple separate analysis tools.
Solution Approach 2:
The patent replaces traditional mechanical/computational methods (finite element analysis, iterative optimization algorithms) with a neural network-based system. The PINN uses automatic differentiation and gradient-based training to solve inverse problems, substituting complex iterative mechanical solvers with a learned model that achieves similar or superior precision with reduced computational overhead.
3Loss of substance
If internal structures are characterized using limited non-destructive measurements, then loss of substance is reduced, but loss of information increases
Solution Approach 1:
The patent implements a feedback mechanism where the PINN iteratively refines its predictions of internal structures by comparing predicted physical fields (stress, temperature, displacement) with actual measurements. The loss function incorporates physics constraints that provide feedback to guide the optimization of geometric parameters, enabling accurate reconstruction despite information loss from limited measurements.
Solution Approach 2:
The patent performs preliminary action by incorporating physical laws and conservation principles into the neural network architecture before actual measurements are taken. The PINN is pre-configured with governing equations (equilibrium equations, constitutive relations) that constrain the solution space, allowing the system to make accurate predictions even with minimal measurement data by leveraging pre-encoded physical knowledge.
Data Source
AI summary
Methods involving physics-informed deep learning to help solve inverse problems of solid materials/structures related to unknown geometry include (1) identifying and characterizing unknown materials/structures and defects with accuracy and predictive capability and limited non-destructive measurements, and/or (2) designing geometrical features and parameters of solid materials and structures to achieve optimized and/or improved performance.