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Anping Wu

As seen in: Nature, Wiley Online Library, MDPI, ACS Publications, The Electronic Library
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Articles by Anping Wu

Transferable reduced order modeling of physical systems via a dilated vector quantized variational autoencoder

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By Yidong Zhang, Hongwei Tang, Yubo Li, Anping Wu, Xing Guo, Feng Liu, Shaoqiu Xiao
| Nature Verified
Abstract Reduced-order models (ROMs) are critically important for the efficient simulation of complex physical systems. However, ROM performance often degrades under out-of-training operating conditions. To overcome this challenge, we propose a transferable reduced-order modeling framework called the dilated vector-quantized variational autoencoder (DVQVAE), which is developed for compact field representation, structural analysis, and adaptation across operating conditions.
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In Situ Label-Free Quantification of Cardiomyocyte Contraction via Holographic Microplatform Click to copy article link Article link copied!

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By Xinyi Dong, Anping Wu, Yaozhen Hou
| ACS Publications
Abstract Click to copy section linkSection link copied! Quantitative, noninvasive assessment of cardiomyocyte contractility is essential for in vitro cardiac models, yet optics offer limited metrics and many quantitative approaches rely on staining or mechanical contact, restricting longitudinal and intratissue analyses.
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Controllable Self-Propulsion of a Biohybrid Millirobot Through Muscle-Fiber-Alignment Programming and Magnetically Assisted Steering

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By Byeongwook Jo, Anping Wu, Zhengyuan Xin
| Wiley Online Library Verified
1 Introduction The rapid advances in biomanufacturing and tissue engineering have enabled engineered muscle tissues to efficiently integrate with compliant artificial structures, driving the accelerated development of muscle-driven biohybrid millirobots as in vitro research platforms and miniature soft actuators. Such systems utilize muscle cells as the actuation module and synthetic materials as the structural framework, thereby recapitulating complex biomechanical behaviors in vitro [1-6].
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Multi-Step Physics-Informed Deep Operator Neural Network for Directly Solving Partial Differential Equations

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By Jing Hui Wang, Yubo Li, Anping Wu, Zheng Chen
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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