TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

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  1. Abueidda, Diab W.; Koric, Seid; Al-Rub, Rashid Abu; Parrott, Corey M.; James, Kai A.; Sobh, Nahil A.: A deep learning energy method for hyperelasticity and viscoelasticity (2022)
  2. Andrew Engel, Zhichao Wang, Anand D. Sarwate, Sutanay Choudhury, Tony Chiang: TorchNTK: A Library for Calculation of Neural Tangent Kernels of PyTorch Models (2022) arXiv
  3. Avazov, Kuldoshbay; Abdusalomov, Akmalbek; Mukhiddinov, Mukhriddin; Baratov, Nodirbek; Makhmudov, Fazliddin; Cho, Young Im: An improvement for the automatic classification method for ultrasound images used on CNN (2022)
  4. Badreddine, Samy; d’Avila Garcez, Artur; Serafini, Luciano; Spranger, Michael: Logic tensor networks (2022)
  5. Bai, Jinshuai; Zhou, Ying; Ma, Yuwei; Jeong, Hyogu; Zhan, Haifei; Rathnayaka, Charith; Sauret, Emilie; Gu, Yuantong: A general neural particle method for hydrodynamics modeling (2022)
  6. Bai, Xiao-Dong; Zhang, Wei: Machine learning for vortex induced vibration in turbulent flow (2022)
  7. Bajaria, Pratik; Yerudkar, Amol; Glielmo, Luigi; Del Vecchio, Carmen; Wu, Yuhu: Self-triggered control of probabilistic Boolean control networks: a reinforcement learning approach (2022)
  8. Bajārs, Jānis; Kozirevs, Filips: Data-driven intrinsic localized mode detection and classification in one-dimensional crystal lattice model (2022)
  9. Bao, Jiakang; He, Yang-Hui; Hirst, Edward; Hofscheier, Johannes; Kasprzyk, Alexander; Majumder, Suvajit: Hilbert series, machine learning, and applications to physics (2022)
  10. Basir, Shamsulhaq; Senocak, Inanc: Physics and equality constrained artificial neural networks: application to forward and inverse problems with multi-fidelity data fusion (2022)
  11. Bihlo, Alex; Popovych, Roman O.: Physics-informed neural networks for the shallow-water equations on the sphere (2022)
  12. Botsas, Themistoklis; Mason, Lachlan R.; Pan, Indranil: Rule-based Bayesian regression (2022)
  13. Bouchnita, Anass; Nony, Patrice; Llored, Jean-Pierre; Volpert, Vitaly: Combining mathematical modeling and deep learning to make rapid and explainable predictions of the patient-specific response to anticoagulant therapy under venous flow (2022)
  14. Boureima, I.; Gyrya, V.; Saenz, J. A.; Kurien, S.; Francois, M.: Dynamic calibration of differential equations using machine learning, with application to turbulence models (2022)
  15. Bridgman, Wyatt; Zhang, Xiaoxuan; Teichert, Greg; Khalil, Mohammad; Garikipati, Krishna; Jones, Reese: A heteroencoder architecture for prediction of failure locations in porous metals using variational inference (2022)
  16. Buhendwa, Aaron B.; Bezgin, Deniz A.; Adams, Nikolaus A.: Consistent and symmetry preserving data-driven interface reconstruction for the level-set method (2022)
  17. Chawshin, Kurdistan; Berg, Carl Fredrik; Varagnolo, Damiano; Lopez, Olivier: Automated porosity estimation using CT-scans of extracted core data (2022)
  18. Chiu, Pao-Hsiung; Wong, Jian Cheng; Ooi, Chinchun; Dao, My Ha; Ong, Yew-Soon: CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method (2022)
  19. Cowen-Rivers, Alexander I.; Lyu, Wenlong; Tutunov, Rasul; Wang, Zhi; Grosnit, Antoine; Griffiths, Ryan Rhys; Maraval, Alexandre Max; Jianye, Hao; Wang, Jun; Peters, Jan; Bou-Ammar, Haitham: \textttHEBO: Pushing the limits of sample-efficient hyper-parameter optimisation (2022)
  20. Coxson, Adam; Mihov, Ivo; Wang, Ziwei; Avramov, Vasil; Barnes, Frederik Brooke; Slizovskiy, Sergey; Mullan, Ciaran; Timokhin, Ivan; Sanderson, David; Kretinin, Andrey; Yang, Qian; Lionheart, William R. B.; Mishchenko, Artem: Machine learning enhanced electrical impedance tomography for 2D materials (2022)

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