Physics-Enhanced Deep Surrogate for High-Dimensional Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional surrogate modeling techniques face challenges with high-dimensional inputs, requiring large numbers of training points and struggling to incorporate physical knowledge efficiently, leading to computational inefficiencies and reduced accuracy.
Innovation Solution
The development of physics-enhanced deep surrogates that combine low-fidelity physics models with neural networks, trained end-to-end to generate approximate inputs for high-fidelity solvers, respecting conservation laws and symmetries, thereby reducing computational costs and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional surrogate modeling techniques are used for high-dimensional inputs, then the model can be trained with available data, but the number of training points required becomes very large and computational cost increases
Solution Approach 1:
The patent introduces a low-fidelity physics model as an intermediary component between the neural network and the final prediction. This physics model acts as a mediator that incorporates domain knowledge and conservation laws, allowing the system to achieve high accuracy with fewer training points by leveraging physical principles rather than relying solely on large datasets
Solution Approach 2:
The patent creates a composite modeling approach by combining neural networks with physics-based models. This hybrid architecture integrates the pattern recognition capabilities of neural networks with the physical consistency of physics-based models, enabling accurate predictions with reduced data requirements while maintaining computational efficiency
2Productivity
If traditional surrogate modeling techniques are used, then the model structure is simple, but the computational efficiency and inference speed are reduced
Solution Approach 1:
The patent segments the modeling process into distinct functional components: a neural network for learning input patterns, a low-fidelity physics model for incorporating physical principles, and a training framework for end-to-end optimization. This segmentation allows each component to specialize in specific tasks, improving overall computational efficiency while maintaining manageable complexity through modular architecture
3Adaptability or versatility
If traditional surrogate modeling techniques are used, then the model can be trained quickly, but the ability to incorporate physical knowledge is limited
Solution Approach 1:
The patent performs preliminary action by pre-formulating physics-based constraints and conservation laws that are embedded into the model architecture before training. The low-fidelity physics model is designed in advance to incorporate domain knowledge, allowing the neural network to learn from fewer examples while respecting physical principles, thereby reducing training time while enhancing physical adaptability
Data Source
AI summary
Surrogate training can include receiving a parameterization of a physical system, where the physical system includes real physical components and the parameterization having corresponding target property in the physical system. The parameterization can be input into a neural network, where the neural network generates a different dimensional parameterization based on the input parameterization. The different dimensional parameterization can be input to a physical model that approximates the physical system. The physical model can be run using the different dimensional parameterization, where the physical model generates an output solution based on the different dimensional parameterization input to the physical model. Based on the output solution and the target property, the neural network can be trained to generate the different dimensional parameterization.