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浸没边界软游泳者运动的神经算子Neural Operators for Immersed-Boundary Soft Swimmers Locomotion

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以下内容为 AI 自动翻译，英文原文附后供参考。

摘要

近期研究引入了神经算子代理，增强了对浸没边界软游泳者的流体动力学场的理解。这种创新方法展示了准确预测变形游泳者（如鳗鱼）运动的能力，在平面和体积模拟中全球相对L^2误差低于20%。研究结果表明，在建模复杂流体-结构相互作用方面取得了显著进展，这对生物启发的机器人技术和流体动力学应用至关重要。

Recent research has introduced neural-operator surrogates that enhance the understanding of hydrodynamic fields in soft swimmers, specifically those with immersed boundaries. This innovative approach demonstrates the capability to accurately predict the locomotion of deforming swimmers, such as eels, with global relative L^2 errors below 20% in both planar and volumetric simulations. The findings suggest significant advancements in modeling complex fluid-structure interactions, which are crucial for applications in bio-inspired robotics and fluid dynamics.

要点

  1. 神经算子代理可以有效建模软游泳者的运动。

    Neural-operator surrogates can effectively model the locomotion of soft swimmers.

  2. 研究在模拟中实现了低于20%的误差，准确性令人印象深刻。

    The research achieved impressive accuracy, with errors below 20% in simulations.

  3. 这项工作对生物启发的机器人技术和流体动力学具有潜在影响。

    This work has potential implications for bio-inspired robotics and fluid dynamics.

为何重要

这项研究在流体-结构相互作用建模方面代表了重要的进步，可以促进机器人和海洋工程的设计改进。

This research represents a significant step forward in the modeling of fluid-structure interactions, which can lead to improved designs in both robotics and marine engineering.

按谁的角度看

产品经理

对于产品经理而言，理解这些进展可以为开发更高效的生物启发机器人系统提供信息，并增强相关领域的产品供应。

For product managers, understanding these advancements can inform the development of more efficient bio-inspired robotic systems and enhance product offerings in related fields.

开发者

开发人员和工程师可以利用这些神经算子模型创建更准确的模拟和流体动力学预测工具，从而改善各种应用的设计和功能。

Developers and engineers can leverage these neural-operator models to create more accurate simulations and predictive tools for fluid dynamics, improving the design and functionality of various applications.

学生与求职者

在机器人、工程和计算流体动力学等领域的学生和求职者可以通过这项研究获得对行业日益相关的前沿建模技术的洞察。

Students and job-seekers in fields like robotics, engineering, and computational fluid dynamics can benefit from this research by gaining insights into cutting-edge modeling techniques that are increasingly relevant in industry.

覆盖来源

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