• Computer Science > Neural and Evolutionary Computing [Submitted on 13 Feb 2026] Title:Enhancing Heat Sink Efficiency in MOSFETs using Physics Informed Neural Networks: A Systematic Study on Coolant Velocity Estimation View PDF HTML (experimental)Abstract:In this work, we present a methodology using Physics Informed Neural Networks (PINNs) to determine the required velocity of a coolant, given inlet and outlet temperatures for a given heat flux in a multilayered metal-oxide-semiconductor field-effect transistor (MOSFET). • MOSFETs are integral components of Power Electronic Building Blocks (PEBBs) and experiences the majority of the thermal load. • Effective cooling of MOSFETs is therefore essential to prevent overheating and potential burnout. • Determining the required velocity for the purpose of effective cooling is of importance but is an ill-posed inverse problem and difficult to solve using traditional methods. • MOSFET consists of multiple layers with different thermal conductivities, including aluminum, pyrolytic graphite sheets (PGS), and stainless steel pipes containing flowing water. • We propose an algorithm that employs sequential training of the MOSFET layers in PINNs.

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  • Computer Science > Neural and Evolutionary Computing [Submitted on 13 Feb 2026] Title:Enhancing Heat Sink Efficiency in MOSFETs using Physics Informed Neural Networks: A Systematic Study on Coolant Velocity Estimation View PDF HTML (experimental)Abstract:In this work, we present a methodology using Physics Informed Neural Networks (PINNs) to determine the required velocity of a coolant, given inlet and outlet temperatures for a given heat flux in a multilayered metal-oxide-semiconductor field-effect transistor (MOSFET). MOSFETs are integral components of Power Electronic Building Blocks (PEBBs

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