Surrogate for Electronic Simulation

Showcasing My Work

Overview

This project aims to develop a surrogate model that predicts thermal behaviour in electronic circuits more quicl;y than traditional simulations.

The project uses machine learning techniques to construct a model that can efficiently approximatee the outcomes of complex thermal simulations. This surrogate model allows forn rapid testing and iteraion in electronic design processes, crucial for thermal management in high-performance electronics.


The model is trained using a dataset generated from an automated data collection script. Training involves adjusting the model weights to minimize the error between the predicted temperatures and actual simulated temperatures.

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