Simulations of biophysical systems have provided a huge contribution to our fundamental understanding of human physiology and remain a central pillar for developments in medical devices and human machine interfaces. However, despite their successes, such simulations usually rely on highly computationally expensive numerical modelling, which is often inefficient to adapt to new simulation parameters. This limits their use in simulating dynamic human behaviours, which typically proceed along a sequence of small time steps. One may painstakingly produce a few static simulations at discretised stages, but not the hundreds of simulations that are essential to capture the dynamic nature of human body. We propose that an alternative approach is to use conditional generative models, which can learn complex relationships between the underlying generative conditions and the output data whilst remaining inexpensive to sample from. As a demonstration of this concept, we present BioMime, a hybrid-structured generative model that combines elements of deep latent variable models and conditional adversarial training. We demonstrate that BioMime can learn to accurately mimic a complex numerical model of human muscle biophysics and then use this knowledge to continuously sample from a dynamically changing system in a short time. This ultimately converts a static model into a dynamic one with no effort. We argue that transfer learning approaches with conditional generative models are a viable solution for dynamic simulation with any numerical model.
翻译:生物物理系统的模拟为我们对人体生理学的基本理解作出了巨大贡献,并且仍然是医学设备和人体机器界面发展的核心支柱。然而,尽管取得了成功,但这种模拟通常依赖高计算、昂贵的数字模型,而这种模型往往无法适应新的模拟参数。这限制了它们在模拟动态人类行为中的使用,而动态人类行为通常是按照一小步的顺序进行。人们可能会在分化阶段艰难地生成一些静态模拟,而不是成百上千的模拟,而这些模拟对于捕捉人体的动态性质至关重要。我们建议,另一种办法是使用有条件的基因模型,这种模型可以学习基本基因条件和产出数据之间的复杂关系,同时从中提取样品。作为这一概念的示范,我们介绍生物模型,即混合结构的基因模型,结合深潜伏变量模型和有条件的对抗训练。我们证明,生物Mime可以学习准确模拟复杂的人类肌肉生物物理模型数字模型,然后利用这一知识在短时间内从动态变化的系统中不断取样。我们最终将一个固定的模型转化为动态的动态模型,我们用一个动态模型来解释一个动态的动态模型。