Neural-Augmented Kelvinlet for Real-Time Soft Tissue Deformation Modeling

TL;DR

This paper introduces a physics-informed neural simulation framework combining Kelvinlet priors with FEM data to enable real-time, accurate, and physically plausible soft-tissue deformation modeling for surgical applications.

Contribution

It presents a novel hybrid approach integrating Kelvinlet-based priors with neural networks for real-time soft tissue simulation, improving accuracy and physical plausibility.

Findings

Enhanced deformation fidelity in surgical tasks

Improved temporal stability over existing methods

Maintained low-latency performance for interactive use

Abstract

Accurate and efficient modeling of soft-tissue interactions is fundamental for advancing surgical simulation, surgical robotics, and model-based surgical automation. To achieve real-time latency, classical Finite Element Method (FEM) solvers are often replaced with neural approximations; however, naively training such models in a fully data-driven manner without incorporating physical priors frequently leads to poor generalization and physically implausible predictions. We present a novel physics-informed neural simulation framework that enables real-time prediction of soft-tissue deformations under complex single- and multi-grasper interactions. Our approach integrates Kelvinlet-based analytical priors with large-scale FEM data, capturing both linear and nonlinear tissue responses. This hybrid design improves predictive accuracy and physical plausibility across diverse neural architectures while maintaining the low-latency performance required for interactive applications. We validate our method on challenging surgical manipulation tasks involving standard laparoscopic grasping tools, demonstrating substantial improvements in deformation fidelity and temporal stability over existing baselines. These results establish Kelvinlet-augmented learning as a principled and computationally efficient paradigm for real-time, physics-aware soft-tissue simulation in surgical AI.