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首页> 外文期刊>Artificial intelligence in medicine >Deep multiphysics: Coupling discrete multiphysics with machine learning to attain self-learning in-silico models replicating human physiology
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Deep multiphysics: Coupling discrete multiphysics with machine learning to attain self-learning in-silico models replicating human physiology

机译：深度多物理场：将离散多物理场与机器学习相结合，以实现复制人类生理学的自学计算机模拟模型

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Objectives: The objective of this study is to devise a modelling strategy for attaining in-silico models replicating human physiology and, in particular, the activity of the autonomic nervous system.Method: Discrete Multiphysics (a multiphysics modelling technique) and Reinforcement Learning (a Machine Learning algorithm) are combined to achieve an in-silico model with the ability of self-learning and replicating feedback loops occurring in human physiology. Computational particles, used in Discrete Multiphysics to model biological systems, are associated to (computational) neurons: Reinforcement Learning trains these neurons to behave like they would in real biological systems.Results: As benchmark/validation, we use the case of peristalsis in the oesophagus. Results show that the in-silico model effectively learns by itself how to propel the bolus in the oesophagus.Conclusions: The combination of first principles modelling (e.g. multiphysics) and machine learning (e.g. Reinforcement Learning) represents a new powerful tool for in-silico modelling of human physiology. Biological feedback loops occurring, for instance, in peristaltic or metachronal motion, which until now could not be accounted for in in-silico models, can be tackled by the proposed technique.
机译：目的：本研究的目的是设计一种建模策略，以获取复制人体生理学，特别是自主神经系统活动的计算机模拟模型。方法：离散多物理场（一种多物理场建模技术）和强化学习（一种（机器学习算法）的组合可实现具有自我学习和复制人类生理学中出现的反馈回路的能力的计算机模拟模型。在离散多物理场中用于对生物系统进行建模的计算粒子与（计算）神经元相关联：强化学习训练这些神经元的行为就像它们在真实生物系统中的行为一样。结果：作为基准/验证，我们使用蠕动的情况食管。结果表明，计算机硅模型可以有效地自行学习如何推动食道推注。结论：第一原理建模（例如多物理场）和机器学习（例如强化学习）的结合代表了一种新的强大的计算机内工具人类生理学建模。所提出的技术可以解决例如在蠕动或超时运动中出现的生物反馈回路，而这种反馈至今仍无法在计算机模型中得到解释。

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