3D Surface Force Prediction Using FIGConv Point Clouds
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Solution Overview
Problem
Techniques to predict forces on or around an object's surface by processing 3D mesh point clouds are computationally expensive.
Innovation Solution
A system that processes 3D mesh point clouds using Factorized Implicit Global Convolution (FIGConv) to predict forces, which involves decomposing 3D images into 2D voxel grids, performing global convolutions, and generating compact representations to predict forces on object surfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computational fluid dynamics methods are used to predict forces on object surfaces, then measurement precision is improved, but computing time and computational complexity increase
Solution Approach 1:
The patent creates a simplified computational copy of the fluid dynamics problem using point cloud representations and neural networks. Instead of solving the full computational fluid dynamics equations, the system trains a neural network model on CFD data and then uses this model to predict forces directly from 3D point clouds, dramatically reducing computing time while maintaining acceptable accuracy
Solution Approach 2:
The patent replaces the traditional mechanical/computational fluid dynamics simulation system with a machine learning-based prediction system. The neural network learns the complex fluid-structure interaction patterns from training data and substitutes the computationally intensive CFD solving process with faster neural network inference
2Measurement precision
If traditional computational fluid dynamics methods are used to predict forces on object surfaces, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex fluid dynamics problem into discrete 3D point cloud representations of the object surface. By dividing the continuous fluid domain into discrete points and using local neighborhood interactions, the system simplifies the computational complexity while maintaining the ability to capture complex flow patterns around the object
Solution Approach 2:
The patent changes the fundamental parameters of the computational approach by transitioning from solving partial differential equations (CFD) to using neural network predictions. This parameter change involves representing the problem in terms of point cloud coordinates and learned feature representations rather than grid-based flow fields, reducing computational complexity
Data Source
AI summary
Apparatuses, systems, and techniques to predict forces associated with an object's surface. In at least one embodiment, forces associated with an object's surface are predicted using factorized implicit global convolution and one or more neural networks.