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RISOn the Performance of a Data-Driven Backward Compatible Physics-Informed Neural Network for Prediction of Flow Past a Cylinder
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: This paper discusses a physics-informed surrogate model aimed at reconstructing the flow field from sparse datasets under a limited computational budget. A benchmark problem of 2D unsteady laminar flow past a cylinder is ...