# DeepOHeat: Operator Learning-based Ultra-fast Thermal Simulation in 3D-IC Design

arXiv: https://arxiv.org/abs/2302.12949 · HTML: https://mlsanity.com/paper/2302.12949/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 25 Feb 2023.

## Builds on (most cited first, 8 of 8)

- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators](https://mlsanity.com/paper/1910.03193/) (2019)
- [Automatic differentiation in machine learning: a survey](https://mlsanity.com/paper/1502.05767/) (2015)
- [Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets](https://mlsanity.com/paper/2103.10974/) (2021)
- [MIONet: Learning multiple-input operators via tensor product](https://mlsanity.com/paper/2202.06137/) (2022)
- [NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework](https://mlsanity.com/paper/2012.07938/) (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)
- [A Thermal Machine Learning Solver For Chip Simulation](https://mlsanity.com/paper/2209.04741/) (2022)

## Built on by (most cited first, 14 of 14)

- [Multi-Resolution Active Learning of Fourier Neural Operators](https://mlsanity.com/paper/2309.16971/) (2023)
- [Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems](https://mlsanity.com/paper/2402.11722/) (2024)
- [Self-Attention to Operator Learning-based 3D-IC Thermal Simulation](https://mlsanity.com/paper/2510.15968/) (2025)
- [Separable Operator Networks](https://mlsanity.com/paper/2407.11253/) (2024)
- [An explainable operator approximation framework under the guideline of Green's function](https://mlsanity.com/paper/2412.16644/) (2024)
- [DeepOHeat-v1: Efficient Operator Learning for Fast and Trustworthy Thermal Simulation and Optimization in 3D-IC Design](https://mlsanity.com/paper/2504.03955/) (2025)
- [SirenFNO: Efficient and Full Frequency Learning of Fourier Neural Operators](https://mlsanity.com/paper/2606.11518/) (2026)
- [Neural Network Surrogate Model for Junction Temperature and Hotspot Position in 3D Multi-Layer High Bandwidth Memory (HBM) Chiplets under Varying Thermal Conditions](https://mlsanity.com/paper/2503.04049/) (2025)
- [Accelerating IC Thermal Simulation Data Generation via Block Krylov and Operator Action](https://mlsanity.com/paper/2510.23221/) (2025)
- [2D-ThermAl: Physics-Informed Framework for Thermal Analysis of Circuits using Generative AI](https://mlsanity.com/paper/2512.01163/) (2025)
- [Physics-Informed Neural Networks for Device and Circuit Modeling: A Case Study of NeuroSPICE](https://mlsanity.com/paper/2512.23624/) (2025)
- [DeepOHeat-v2: Self-Improving Operator Learning for Fast and Trustworthy Thermal Optimization in 3D-IC Design](https://mlsanity.com/paper/2608.16080/) (2026)
- [TherMapNet Attention-Guided Runtime Full-Chip Thermal Map Prediction from Performance Metrics](https://mlsanity.com/paper/2608.21887/) (2026)
- [A Composable AI-Accelerated Iterative Solver for 3D-IC Thermal Modeling](https://mlsanity.com/paper/2610.02461/) (2026)

## Abstract (arXiv)

Thermal issue is a major concern in 3D integrated circuit (IC) design. Thermal optimization of 3D IC often requires massive expensive PDE simulations. Neural network-based thermal prediction models can perform real-time prediction for many unseen new designs. However, existing works either solve 2D temperature fields only or do not generalize well to new designs with unseen design configurations (e.g., heat sources and boundary conditions). In this paper, for the first time, we propose DeepOHeat, a physics-aware operator learning framework to predict the temperature field of a family of heat equations with multiple parametric or non-parametric design configurations. This framework learns a functional map from the function space of multiple key PDE configurations (e.g., boundary conditions, power maps, heat transfer coefficients) to the function space of the corresponding solution (i.e., temperature fields), enabling fast thermal analysis and optimization by changing key design configurations (rather than just some parameters). We test DeepOHeat on some industrial design cases and compare it against Celsius 3D from Cadence Design Systems. Our results show that, for the unseen testing cases, a well-trained DeepOHeat can produce accurate results with 1000× to 300000× speedup.