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Summary

The research presents a thermal machine-learning solver which functions to speed up on-chip thermal simulation processes because it handles the long simulation times which occur when using FEM and CFD methods. The authors use the CoAEMLSim framework to develop a solution method which handles both fixed and variable heat transfer coefficients for realistic chip-level applications. The proposed ML-based solver receives validation through tests against both commercial physics-based solver Ansys MAPDL and neural-network baseline UNet which show better results in accuracy and scalability and generalization ability. The research contributes to the field through evidence that physics-based machine learning methods decrease simulation duration without compromising results which allows designers to perform thermal-aware electronic system design at increased speeds.

Major issues

The paper demonstrates its validation through commercial solver comparison but it must describe the physical limits which the ML solver operates under. The model requires direct documentation which explains its physical consistency maintenance through energy conservation and boundary condition adherence.

The research needs better evidence about training and testing distribution details because it lacks clear information about validation scenario differences from training data.

The computational cost of training the ML solver is not discussed in sufficient detail. The evaluation process between training expenses and inference performance gains needs more information to enable readers understand the full financial effects of this method.

Minor issues

The presentation of the CoAEMLSim extension needs improvement through either a high-level architectural diagram or a step-by-step guide which would help readers who lack knowledge about the original framework.

The experimental section requires better organization through the combination of specific details about dataset size and mesh resolution and boundary condition variability.

The technical sections require only fundamental language changes and stylistic modifications to improve their readability because the methodology section contains the most intricate information.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.