<ul data-eligibleForWebStory="true"><li>A novel Trunk-Branch (TB)-net physics-informed neural network (PINN) architecture has been developed to solve complex problems in porous mediums.</li><li>The TB-net PINN incorporates trunk and branch nets to capture global and local features, aiming to address forward flow, forward heat transfer, inverse heat transfer, and transfer learning problems.</li><li>The architecture uses a Fully-connected Neural Network (FNN) as the trunk net and separate FNNs as branch nets, with automatic differentiation for partial derivatives of outputs, considering various physical loss.</li><li>The TB-net PINN architecture demonstrated effectiveness, flexibility, and potential for practical engineering applications by solving forward problems and showcasing resource reuse in transfer learning.</li></ul>

A novel Trunk Branch-net PINN for flow and heat transfer prediction in porous medium

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