AI RESEARCH
Prediction of Steady-State Flow through Porous Media Using Machine Learning Models
arXiv CS.LG
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ArXi:2603.06762v1 Announce Type: cross Solving flow through porous media is a crucial step in the topology optimisation of cold plates, a key component in modern thermal management. Traditional computational fluid dynamics (CFD) methods, while accurate, are often prohibitively expensive for large and complex geometries. In contrast, data-driven surrogate models provide a computationally efficient alternative, enabling rapid and reliable predictions.