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Showing 1 to 3 of 3 for “"Deep Operator Networks"”.

  1. Acceleration of combustion computation fluid dynamics simulations through machine learning

    … computational fluid dynamics simulations through Deep Operator Networks (DeepONets), achieving up to an 18 times speedup in the chemical source term evaluation. Another numerical experiment showed an overall reduction of approximately 30% in computation time. This was achieved by directly …

    uiuc Repository record for Acceleration of combustion computation fluid dynamics simulations through machine learning (opens in a new tab)

  2. Operator learning in the overparameterized regime

    Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01

    uiuc Repository record for Operator learning in the overparameterized regime (opens in a new tab)

  3. Application of data-driven neural networks to bio-inspired lattice design and prediction of multiphysics solution fields

    … decisions. This study develops conventional deep neural networks and deep operator-learning surrogates that predict full temperature and stress fields from process and geometry inputs, accelerating evaluation by orders of magnitude relative to finite element analysis (FEA). Bio-inspired, …

    uiuc Repository record for Application of data-driven neural networks to bio-inspired lattice design and prediction of multiphysics solution fields (opens in a new tab)