Soft Body Simulator Using Neural Networks for Real-Time Deformation
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Solution Overview
Problem
Current robotic and computer-assisted surgical training methods lack realistic and efficient simulation of soft body deformations, leading to potential complications during surgery due to limited training capabilities and high computational costs.
Innovation Solution
A soft body simulator utilizing machine learning models, specifically neural networks, to accurately and realistically simulate soft body deformations in real-time, adapting to the user's skill level and reducing computational burden by separating deformation rendering into local, global, and dynamic responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computational methods are used to simulate soft body deformations, then simulation accuracy can be maintained, but computational cost becomes prohibitively high
Solution Approach 1:
The patent creates simplified copies of the complex soft body deformation physics through pre-computed deformation fields and machine learning models. Instead of performing full computational simulations during interaction, the system uses pre-trained neural networks that replicate deformation behavior with fraction of the computational cost, maintaining visual fidelity while dramatically reducing processing requirements
Solution Approach 2:
The system performs preliminary computation by pre-training machine learning models offline with large datasets of soft body simulations. This preliminary action transfers complex computational work from runtime interactions to offline model training, enabling fast real-time predictions during actual surgical simulation without sacrificing accuracy
2Productivity
If real-time simulation of soft body deformations is implemented, then training effectiveness improves, but computational burden increases
Solution Approach 1:
The patent replaces traditional mechanical physics-based simulation systems with machine learning-based predictive models. The neural networks learn deformation patterns from training data and directly predict deformation outcomes without solving complex differential equations in real-time, substituting computational mechanics with statistical inference that is both faster and more efficient
Solution Approach 2:
The simulation system is segmented into distinct components: pre-processing of training data, training phase of neural networks, and inference phase during interaction. This segmentation allows computationally intensive work to be performed offline during training, while runtime operations use lightweight model inference that maintains real-time performance
3Reliability
If high-fidelity soft body simulation is provided, then surgical training quality improves, but system complexity increases
Solution Approach 1:
The system changes parameters from continuous physics-based calculations to discrete neural network predictions. By representing soft body deformation through learned parameter mappings rather than full physics simulations, the system achieves high-fidelity results with reduced computational complexity and simpler runtime operations
Data Source
AI summary
A simulator for simulating soft body deformation includes a display system, a user interface, and a controller. The controller includes one or more processors coupled to memory that stores instructions that when executed cause the system to perform operations. The operations include generating a simulated soft body having a shape represented, at least in part, by a plurality of nodes. The operations further include determining, with at least a first machine learning model, a displacement of individual nodes included in the plurality of nodes in response to a simulated force applied to the simulated soft body. The operations further include rendering a deformation of the simulated soft body in substantially real time in response to the simulated force based, at least in part, on the displacement.