Simulations of biophysical systems are fundamental for studying physiological mechanisms and developing human machine interfaces. Whilst advanced numerical methods, such as finite element models, can excel in this task, they are extremely computationally expensive to use when generating a large number of simulations or simulating dynamic events with continuously changing structural parameters. We propose an architecture that uses a conditional generative model to interpolate between the numerical model states, dramatically lowering the modeling time while maintaining a high generation accuracy. As a demonstration of this concept, we present BioMime, a hybrid-structured generative model that enables an accurate, ultra-fast, and arbitrarily high temporal-resolution simulation of a specific biophysical system during dynamic changes. This methodology has wide applications in physiological and clinical research as well as in supporting data augmentation strategies for signal analysis, representing a computationally efficient and highly accurate model for biophysical simulations.

翻译：生物物理系统的仿真对于研究生理机制和开发人机接口至关重要。尽管有限元模型等先进数值方法在此类任务中表现出色，但在生成大量仿真结果或模拟结构参数持续变化的动态事件时，其计算成本极高。我们提出了一种架构，采用条件生成模型对数值模型状态进行插值，从而在保持高生成精度的前提下大幅降低建模时间。为验证这一概念，我们展示了BioMime——一种混合结构生成模型，能够在动态变化过程中对特定生物物理系统实现精确、超快速且任意高时间分辨率的仿真。该方法在生理学与临床研究领域具有广泛应用，并可支持信号分析中的数据增强策略，成为兼顾计算效率与高精度的生物物理仿真模型。