Machine-Learned Physics Model for Rapid Design Iteration
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Solution Overview
Problem
Physics simulation engines are computationally intensive, leading to lengthy simulation times and limited design iteration capabilities, requiring significant computing resources and restricting the ability to quickly test multiple design changes.
Innovation Solution
A computer-implemented method using machine-learned physics prediction models to generate updated physics simulation data by inputting values from neighboring cells, allowing for near-real-time iterative design updates through region-based updates, leveraging machine learning for efficient prediction and reduced computational resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physics simulation engines are used to simulate physical characteristics of virtual components, then simulation precision is improved, but computational time and resource consumption increase significantly
Solution Approach 1:
The patent creates a machine-learned copy of the physics simulation engine that can predict physical characteristics without performing full physics simulations. The model is trained on simulation data and then used to generate predictions that approximate the behavior of the original physics engine, significantly reducing computational time while maintaining acceptable precision for design iteration purposes
Solution Approach 2:
The patent performs preliminary action by training the machine-learned model in advance using data from full physics simulations. Once trained, the model can quickly predict outcomes for new design configurations without requiring time-consuming physics simulations, enabling rapid design iteration
2Measurement precision
If full physics simulations are performed for each design iteration, then simulation accuracy is maintained, but design iteration speed decreases
Solution Approach 1:
The system creates a machine-learned copy that approximates the physics simulation behavior, enabling rapid design iteration. The model is trained on simulation data and then used to predict outcomes for design changes, maintaining sufficient accuracy for comparative analysis while dramatically improving iteration speed
Solution Approach 2:
The patent performs partial action by using a machine-learned model that captures the essential physics behavior without performing complete physics simulations. This partial approach provides sufficient accuracy for design iteration purposes while achieving the speed improvements needed for productive design work
3Reliability
If significant computing resources are allocated to physics simulations, then simulation quality is improved, but resource efficiency deteriorates
Solution Approach 1:
The patent creates a machine-learned copy that consumes minimal computational resources during inference compared to full physics simulations. The model is trained using simulation data but then executes rapidly with minimal resource requirements, enabling quality predictions at low computational cost
Solution Approach 2:
The patent changes the computational parameters from performing full physics simulations to using a pre-trained machine-learned model. This parameter change dramatically reduces computational resource consumption while maintaining sufficient simulation quality for design iteration purposes
Data Source
AI summary
The present disclosure provides systems and methods that expedite the design of physical components through the use of iterative and computationally efficient virtual simulations. In particular, the systems and methods of the present disclosure can be used as part of an iterative design process in which a product designer is able to iteratively make changes to a component design by iteratively interacting a visualization of a virtual representation of the component within a virtual environment.