CVSim-6生理系统物理信息重建中的总不确定性量化
Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology
AI总结：
本研究针对生理系统物理信息重建的多源不确定性问题，提出基于随机投影与蒙特卡洛采样的MC X-TFC方法，在CVSim-6模型上验证了其在数据受限、含噪及模型误设场景下的鲁棒估计能力。
摘要：
在通过仿真预测物理现象时，量化由多种来源导致的总不确定性与确保基础数值模型的准确性同等重要。可能的不确定性来源包括：数据噪声导致的不可约随机不确定性，数据不足或参数化不当引发的认知不确定性，以及与使用错误设定的模型方程相关的模型形式不确定性。基于物理的正则化与随机不确定性、认知不确定性、模型形式不确定性及其组合存在复杂的相互作用，为提升实际工况下运行的物理信息数字孪生的预测性能，亟需更好地理解这种相互作用。\n本研究聚焦生物与生理模型，探究了微分系统状态与参数估计过程中的总不确定性分解，所采用的方法为MC X-TFC——一种基于随机投影和蒙特卡洛采样的新型物理信息不确定性量化方法。研究将MC X-TFC应用于六室刚性常微分方程系统，即基于人体生理学开发的CVSim-6模型。研究通过逐步减少数据量同时增加待估计参数数量，以及考察肺室非线性阻力存在模型形式错误设定时的总不确定性，对该系统展开分析。我们重点研究了差异项的公式构建与模型形式不确定性量化之间的相互作用，并证明了额外物理信息如何对估计过程提供帮助。该方法在估计未知状态和参数方面展现出鲁棒性与高效性，即便在数据有限、稀疏且含噪声的情况下依然有效。它还在融合数据与物理信息以提升估计效果方面具备高度灵活性，即使在模型错误设定的场景下也能适用。
英文摘要：
When predicting physical phenomena through simulation, quantification of the total uncertainty due to multiple sources is as crucial as making sure the underlying numerical model is accurate. Possible sources include irreducible aleatoric uncertainty due to noise in the data, epistemic uncertainty induced by insufficient data or inadequate parameterization, and model-form uncertainty related to the use of misspecified model equations. Physics-based regularization interacts in nontrivial ways with aleatoric, epistemic and model-form uncertainty and their combination, and a better understanding of this interaction is needed to improve the predictive performance of physics-informed digital twins that operate under real conditions. With a specific focus on biological and physiological models, this study investigates the decomposition of total uncertainty in the estimation of states and parameters of a differential system simulated with MC X-TFC, a new physics-informed approach for uncertainty quantification based on random projections and Monte-Carlo sampling. MC X-TFC is applied to a six-compartment stiff ODE system, the CVSim-6 model, developed in the context of human physiology. The system is analyzed by progressively removing data while estimating an increasing number of parameters and by investigating total uncertainty under model-form misspecification of non-linear resistance in the pulmonary compartment. In particular, we focus on the interaction between the formulation of the discrepancy term and quantification of model-form uncertainty, and show how additional physics can help in the estimation process. The method demonstrates robustness and efficiency in estimating unknown states and parameters, even with limited, sparse, and noisy data. It also offers great flexibility in integrating data with physics for improved estimation, even in cases of model misspecification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
