<ul><li>A novel physics-informed neural simulator is introduced for fast and accurate simulation of soft tissue deformations, crucial for surgical robotics and medical training.</li><li>The framework integrates Kelvinlet-based priors into neural simulators, combining Kelvinlets for residual learning and regularization in data-driven soft tissue modeling.</li><li>Incorporating large-scale Finite Element Method (FEM) simulations of linear and nonlinear soft tissue responses, the method enhances neural network predictions, improving accuracy, and physical consistency with low latency for real-time performance.</li><li>The approach demonstrates effectiveness in accurate surgical maneuvers mimicking standard laparoscopic tissue grasping tools, highlighting Kelvinlet-augmented learning as a powerful strategy for physics-aware soft tissue simulation in surgical applications.</li></ul>

Neural-Augmented Kelvinlet: Real-Time Soft Tissue Deformation with Multiple Graspers

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