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@ARTICLE{Puri:1029324,
      author       = {Puri, Rishabh and Onishi, Junya and Rüttgers, Mario and
                      Sarma, Rakesh and Tsubokura, Makoto and Lintermann, Andreas},
      title        = {{O}n the choice of physical constraints in artificial
                      neural networks for predicting flow fields},
      journal      = {Future generation computer systems},
      volume       = {161},
      issn         = {0167-739X},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2024-05051},
      pages        = {361 - 375},
      year         = {2024},
      abstract     = {The application of Artificial Neural Networks (ANNs) has
                      been extensively investigated for fluid dynamic problems. A
                      specific form of ANNs are Physics-Informed Neural Networks
                      (PINNs). They incorporate physical laws in the training and
                      have increasingly been explored in the last few years. In
                      this work, the prediction accuracy of PINNs is compared with
                      that of conventional Deep Neural Networks (DNNs). The
                      accuracy of a DNN depends on the amount of data provided for
                      training. The change in prediction accuracy of PINNs and
                      DNNs is assessed using a varying amount of training data. To
                      ensure the correctness of the training data, they are
                      obtained from analytical and numerical solutions of
                      classical problems in fluid mechanics. The objective of this
                      work is to quantify the fraction of training data relative
                      to the maximum number of data points available in the
                      computational domain, such that the accuracy gained with
                      PINNs justifies the increased computational cost.
                      Furthermore, the effects of the location of sampling points
                      in the computational domain and noise in training data are
                      analyzed. In the considered problems, it is found that PINNs
                      outperform DNNs when the sampling points are positioned in
                      the Regions of Interest. PINNs for predicting potential flow
                      around a Rankine oval have shown a better robustness against
                      noise in training data compared to DNNs. Both models show
                      higher prediction accuracy when sampling points are randomly
                      positioned in the flow domain as compared to a prescribed
                      distribution of sampling points. The findings reveal new
                      insights on the strategies to massively improve the
                      prediction capabilities of PINNs with respect to DNNs.},
      cin          = {JSC},
      ddc          = {004},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / JLESC - Joint
                      Laboratory for Extreme Scale Computing (JLESC-20150708) /
                      RAISE - Research on AI- and Simulation-Based Engineering at
                      Exascale (951733)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel1)JLESC-20150708 /
                      G:(EU-Grant)951733},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001281744600001},
      doi          = {10.1016/j.future.2024.07.009},
      url          = {https://juser.fz-juelich.de/record/1029324},
}