# A Thermal Machine Learning Solver For Chip Simulation

arXiv: https://arxiv.org/abs/2209.04741 · HTML: https://mlsanity.com/paper/2209.04741/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 10 Sep 2022.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations](https://mlsanity.com/paper/1708.00588/) (2017)
- [NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework](https://mlsanity.com/paper/2012.07938/) (2020)
- [DiscretizationNet: A Machine-Learning based solver for Navier-Stokes Equations using Finite Volume Discretization](https://mlsanity.com/paper/2005.08357/) (2020)
- [An unsupervised learning approach to solving heat equations on chip based on Auto Encoder and Image Gradient](https://mlsanity.com/paper/2007.09684/) (2020)
- [Thermal and IR Drop Analysis Using Convolutional Encoder-Decoder Networks](https://mlsanity.com/paper/2009.09009/) (2020)
- [A composable autoencoder-based iterative algorithm for accelerating numerical simulations](https://mlsanity.com/paper/2110.03780/) (2021)

## Built on by (most cited first, 7 of 7)

- [DeepOHeat: Operator Learning-based Ultra-fast Thermal Simulation in 3D-IC Design](https://mlsanity.com/paper/2302.12949/) (2023)
- [Self-Attention to Operator Learning-based 3D-IC Thermal Simulation](https://mlsanity.com/paper/2510.15968/) (2025)
- [DeepOHeat-v1: Efficient Operator Learning for Fast and Trustworthy Thermal Simulation and Optimization in 3D-IC Design](https://mlsanity.com/paper/2504.03955/) (2025)
- [Physics-Informed Laplace Neural Operator for Solving Partial Differential Equations](https://mlsanity.com/paper/2602.12706/) (2026)
- [CapBench: A Multi-PDK Dataset for Machine-Learning-Based Post-Layout Capacitance Extraction](https://mlsanity.com/paper/2604.11202/) (2026)
- [A domain decomposition-based autoregressive deep learning model for unsteady and nonlinear partial differential equations](https://mlsanity.com/paper/2408.14461/) (2024)
- [DeepOHeat-v2: Self-Improving Operator Learning for Fast and Trustworthy Thermal Optimization in 3D-IC Design](https://mlsanity.com/paper/2608.16080/) (2026)

## Abstract (arXiv)

Thermal analysis provides deeper insights into electronic chips behavior under different temperature scenarios and enables faster design exploration. However, obtaining detailed and accurate thermal profile on chip is very time-consuming using FEM or CFD. Therefore, there is an urgent need for speeding up the on-chip thermal solution to address various system scenarios. In this paper, we propose a thermal machine-learning (ML) solver to speed-up thermal simulations of chips. The thermal ML-Solver is an extension of the recent novel approach, CoAEMLSim (Composable Autoencoder Machine Learning Simulator) with modifications to the solution algorithm to handle constant and distributed HTC. The proposed method is validated against commercial solvers, such as Ansys MAPDL, as well as a latest ML baseline, UNet, under different scenarios to demonstrate its enhanced accuracy, scalability, and generalizability.