Applications of physics informed neural operators by Shawn G Rosofsky, Hani Al Majed, E A Huerta

Subjects: “…physics informed deep learning…”
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Machine learning meets Kepler: inverting Kepler’s equation for All vs All conjunction analysis by Kevin Otto, Simon Burgis, Kristian Kersting, Reinhold Bertrand, Devendra Singh Dhami

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Physics-Informed Deep Learning for Traffic State Estimation: A Survey and the Outlook by Xuan Di, Rongye Shi, Zhaobin Mo, Yongjie Fu

Subjects: “…physics-informed deep learning (PIDL)…”
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Physics-informed deep learning for one-dimensional consolidation by Yared W. Bekele

Subjects: “…Physics-informed deep learning…”
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Kinetic model-informed deep learning for multiplexed PET image separation by Bolin Pan, Paul K. Marsden, Andrew J. Reader

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Physics-informed generative neural network: an application to troposphere temperature prediction by Zhihao Chen, Jie Gao, Weikai Wang, Zheng Yan

Subjects: “…physics-informed deep learning…”
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Recipes for when physics fails: recovering robust learning of physics informed neural networks by Chandrajit Bajaj, Luke McLennan, Timothy Andeen, Avik Roy

Subjects: “…Physics informed deep learning…”
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The Old and the New: Can Physics-Informed Deep-Learning Replace Traditional Linear Solvers? by Stefano Markidis 

Subjects: “…physics-informed deep-learning…”
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Kinematics-aware multigraph attention network with residual learning for heterogeneous trajectory prediction by Zihao Sheng, Zilin Huang, Sikai Chen

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Explicit Physics-Informed Deep Learning for Computer-Aided Diagnostic Tasks in Medical Imaging by Shira Nemirovsky-Rotman, Eyal Bercovich

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Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems by Xiaofei Guan, Xintong Wang, Hao Wu, Zihao Yang, Peng Yu

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Physics-Informed Deep Learning for Traffic State Estimation: Illustrations With LWR and CTM Models by Archie J. Huang, Shaurya Agarwal

Subjects: “…Physics informed deep learning…”
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PhyICNet: Physics-informed interactive learning convolutional recurrent network for spatiotemporal dynamics by Ruohan Cao, Jin Su, Jinqian Feng, Qin Guo

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Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning by Shi Zeng, Dechang Pi

Subjects: “…physics-informed deep learning…”
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Physics-Informed Deep Learning for Muscle Force Prediction With Unlabeled sEMG Signals by Shuhao Ma, Jie Zhang, Chaoyang Shi, Pei Di, Ian D. Robertson, Zhi-Qiang Zhang

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Enhancing Computational Accuracy in Surrogate Modeling for Elastic–Plastic Problems by Coupling S-FEM and Physics-Informed Deep Learning by Meijun Zhou, Gang Mei, Nengxiong Xu

Subjects: “…physics-informed deep learning…”
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Physics driven interpretable deep learning-based insights into boiling crisis of smooth and roughened surfaces by Uzair Sajjad, Sadaf Mehdi, Imtiyaz Hussain, Tauseef-ur Rehman, Muhammad Sultan, Mohammad Mehdi Rashidi, Wei-Mon Yan

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On the Limitations of Physics-Informed Deep Learning: Illustrations Using First-Order Hyperbolic Conservation Law-Based Traffic Flow Models by Archie J. Huang, Shaurya Agarwal

Subjects: “…Physics-informed deep learning…”
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Novel Hybrid Physics-Informed Deep Neural Network for Dynamic Load Prediction of Electric Cable Shovel by Tao Fu, Tianci Zhang, Yunhao Cui, Xueguan Song

Subjects: “…Hybrid physics-informed deep learning…”
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A hybrid physics-data-knowledge driven approach for human-machine hybrid-augmented intelligence-based system management and control by Jun ZHANG, Peidong XU, Siyuan CHEN, Tianlu GAO, Yuxin DAI, Ke ZHANG, Hang ZHAO, Jiemai GAO, Yuyang BAI, Jinxing LI, Haoran ZHANG, Xiang LI, Jiuxiang CHEN

Subjects: “…hybrid physics-data-knowledge driven approach;human-machine hybrid-augmented intelligence;system management and control;physics-informed deep learning;causal analysis;interpretable AI;virtual digital human…”
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