Physics‐informed deep learning for T2‐deblurred superresolution turbo spin echo MRIdeblurringdeep learningmagnetic resonance imagingphysics‐based modelsuperresolutionturbo spin echoPurpose Deep learning super
高精度数据和低精度数据通过独特设计的网络结构,把两个数据同时用到一起。 47:54 deeponet 和传统方法 两者对比,deeponet 可以用更少的数据。 52:22 对于复杂问题 可以分块学习然后再整合到一起。 53:52 问题 非线性 模型难以训练 真正的复杂问题难以求解 数据生成 和理论保证。 58:38 细节 先训练数据 再所...
Raissi, Maziar, Paris Perdikaris, and George Em Karniadakis. "Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations." arXiv preprint arXiv:1711.10566 (2017). Citation @article{raissi2019physics, title={Physics-informed neural networks: A deep l...
Physics informed deep learning (part i): Data-driven solutions of nonlinear partial differential equations. arXiv 2017. arXiv preprint arXiv:1711.10561. Google Scholar 8 Raissi, M., Perdikaris, P., & Karniadakis, G.E. Physics Informed Deep Learning (Part II): Data-driven Discovery of ...
of deep learning (Part I)Dynamic system and optimal control perspective of deep learning (Part II...
Thus, this novel, physics-informed deep learning framework represents a superior approach over the traditional material decomposition process, both on objective (i.e. reduced error rate) and subjective (i.e. visual inspection) scales. Our results indicate that the visibility of anatomy and pathology...
Physics informed deep learning (part i): data-driven solutions of nonlinear partial differential equations arXiv preprint arXiv:1711.10561 (2017) Google Scholar [20] M. Raissi, P. Perdikaris, G.E. Karniadakis Physics informed deep learning (part ii): data-driven discovery of nonlinear partial ...
E. Physics informed deep learning (Part II): Data-driven discovery of nonlinear partial differential equations. Preprint at https://arxiv.org/abs/1711.10566v1 (2017). Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. Preprint at https://arxiv.org/abs/1312.6114v10 (2013). ...
1月25日,南京理工大学光电信息科学与工程学院左超教授,陈钱教授,冯世杰副教授,胡岩副教授,香港大学电气电子工程系林彥民教授等组成的团队,在期刊 Opto-Electronic Advances 发表题为 Physics-informed deep learning for fringe pattern analysis 的论文。
Paper presented at the SPE Reservoir Simulation Conference, On-Demand, October 2021. 这篇论文关注石油储藏模拟问题,应用PINN解决该领域的问题,并对标准PINN进行了改进来适应本领域的问题。显然,本篇论文专业性较强,很多东西我未能理解。因此对于本篇工作我只有一个大概模糊的理解:对于标准PINN,作者从物理规律方面...