Multiscale modeling meets machine learning: What can we learn?
Multiscale modeling meets machine learning: What can we learn?
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DOI：
10.1007/s11831-020-09405-5
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发表时间：
2021-05
期刊：
影响因子：
--
通讯作者：
Kuhl E
中科院分区：
文献类型：
--
作者：
Peng GCY;Alber M;Tepole AB;Cannon WR;De S;Dura-Bernal S;Garikipati K;Karniadakis G;Lytton WW;Perdikaris P;Petzold L;Kuhl E
机器学习在生物、生物医学和行为科学中越来越被认为是一种很有前途的技术。毫无疑问，这种技术在图像识别方面是令人难以置信的成功，在包括电生理学、放射学或病理学在内的诊断中立即应用，在这些诊断中，我们可以访问大量的注释数据。然而，机器学习在预测方面往往表现不佳，特别是在处理稀疏数据时。在这个领域，基于经典物理的模拟似乎仍然是不可替代的。在这篇综述中，我们确定了在生物医学科学中机器学习和多尺度建模可以相互受益的领域：机器学习可以将基于物理的知识以控制方程、边界条件或约束的形式整合起来，以管理不良发布的问题，并稳健地处理稀疏和噪声数据；多尺度建模可以整合机器学习来创建替代模型，识别系统动力学和参数，分析敏感度，并量化不确定性，以桥接尺度和理解函数的出现。着眼于生命科学中的应用，我们讨论了机器学习和多尺度建模相结合的技术现状，确定了应用和机会，提出了开放问题，并解决了潜在的挑战和限制。我们预计，它将在计算力学社区内引发讨论，并接触到其他学科，包括数学、统计学、计算机科学、人工智能、生物医学、系统生物学和精确医学，以联合起来为生物系统建立稳健和高效的模型。
Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.
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