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thermal-modeling

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1D thermal resistance network for CPU cold plate optimization, calibrated against CFD simulations. Enables rapid parametric design studies to identify thermal bottlenecks and evaluate design modifications without re-running expensive 3D CFD.

  • Updated Dec 1, 2025
  • Python

The repository applies Bayesian inference, Gaussian processes, Kalman filters, and particle filters to detect and track temperature hotspots across space and time. The methods are evaluated on simulated sensor data to estimate hotspot location, intensity, and temporal dynamics.

  • Updated Apr 14, 2026
  • Python

A physics-informed neural network approach is proposed for control-oriented building thermal modeling, combining data with physical laws to improve accuracy and reduce training data needs. The method predicts room temperature, power use, and hidden thermal states more reliably than standard neural networks.

  • Updated Apr 18, 2026
  • Python

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