AI 中文总结
研究从活性物质连续体模型的流体流动推断活性的逆问题，利用深度学习制定广义流体动力学反演框架，应用于两个基石范式，通过流体流场动能谱推断模型参数，为模型推断和选择提供有原则的方法。
AI 中文摘要
活性物质系统通过局部微观尺度的能量耗散偏离热力学平衡。虽然流体动力学连续体框架在模拟这些非平衡现象（正向问题）方面非常成功，但直接测量活性应力的困难从根本上限制了对现实世界活性材料的表征。本文利用深度学习解决逆问题：从活性流体的可观测流场数据进行模型推断和模型选择。我们制定了一个广义的流体动力学反演框架，应用于活性连续体物理学的两个基石范式：活性模型H（代表标量活性物质）和活性向列相（代表具有取向序的活性系统）。我们证明，从流体流场获得的动能谱保留了用于推断活性模型H和活性向列相参数的高保真活性特征。我们的深度学习方法为在活性物质连续体模型中给定流场数据时处理模型推断和选择问题提供了一种有原则的方法。
英文摘要
Active matter systems are driven out of thermodynamic equilibrium by localized, microscale energy dissipation. While hydrodynamic continuum frameworks are highly successful at simulating these non-equilibrium phenomena (the forward problem), characterizing real-world active materials is fundamentally bottlenecked by the difficulty of measuring active stresses directly. This paper addresses the inverse problem using deep learning: model inference and model selection from observable flow field data of active fluids. We formulate a generalized hydrodynamic inversion framework applied to two cornerstone paradigms of active continuum physics: Active Model H (representing scalar active matter) and Active Nematics (representing active systems with orientational order). We demonstrate that the kinetic energy spectrum obtained from the fluid flow fields preserve a high-fidelity signature of activity to infer parameters of active model H and active nematics. Our deep learning method presents a principled way to bear upon questions of model inference and selection given the flow field data in continuum models of active matter.
Comments9 pages and 8 figures
