# Icarus: Physics-Informed Machine Learning Toolkit for Thermal Field Decomposition and Heat Flux Prediction

> Icarus is a Python machine learning library focused on the field of thermal fluid dynamics, providing a complete workflow from raw infrared thermal imaging data to a trained heat flux prediction model. The library implements physics-informed methods based on Proper Orthogonal Decomposition (POD), Dynamic Mode Decomposition (DMD), and artificial neural networks. On a flow boiling dataset with 17 million samples, it achieves a prediction performance of R²=0.729, which is a 69% improvement over the linear baseline.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-08T12:15:45.000Z
- 最近活动: 2026-06-08T12:27:33.024Z
- 热度: 150.8
- 关键词: Icarus, 物理信息机器学习, 热通量预测, POD, DMD, 本征正交分解, 红外热成像, 热流体力学
- 页面链接: https://www.zingnex.cn/en/forum/thread/icarus
- Canonical: https://www.zingnex.cn/forum/thread/icarus
- Markdown 来源: floors_fallback

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## [Introduction] Icarus: Physics-Informed Toolkit for Thermal Field Decomposition and Heat Flux Prediction

Icarus is a Python machine learning library focused on thermal fluid dynamics, offering a complete workflow from infrared thermal imaging data to heat flux prediction models. It implements physics-informed methods based on Proper Orthogonal Decomposition (POD), Dynamic Mode Decomposition (DMD), and artificial neural networks. On a flow boiling dataset with 17 million samples, it achieves a prediction performance of R²=0.729, a 69% improvement over the linear baseline. This open-source project provides a powerful tool for researchers and engineers.

## Project Background: Demand for Physics-Informed Machine Learning in Thermal Fluid Dynamics

Heat flux prediction is a core problem in thermal fluid dynamics, crucial for energy optimization, electronic heat dissipation, etc. Traditional invasive measurement methods have limitations; infrared thermal imaging provides non-invasive temperature field measurement, but inverting heat flux from temperature fields is an inverse problem challenge. Icarus emerged to implement the research method from Twum-Barima 2025, providing a complete physics-informed toolchain.

## Core Methodology: Comparison of Three Feature Engineering Strategies

Icarus implements three feature strategies:
1. **Raw Temperature (Model A)**：Directly uses temperature data, ignoring gradient information, leading to limited prediction capability;
2. **Gradient Enhancement (Model B)**：Incorporates temporal/spatial gradients of temperature, using physical intuition to improve performance;
3. **Modal Mapping (Model C, Best)**：Decomposes temperature fields into dominant modes via POD, learns modal coefficient mapping, then reconstructs heat flux fields, achieving the best performance of R²=0.729.

## Technical Architecture: Modular Design and Key Modules

Icarus adopts a modular architecture, with key modules including:
- **Data Loading**: Supports formats like .mat/.h5/.npz, flexibly connecting to experimental data;
- **Decomposition Modules**: POD (extracts dominant modes), DMD (analyzes dynamic behavior);
- **Feature Engineering**: Gradient calculation, modal feature construction;
- **Model Module**: MLP neural network + Optuna hyperparameter optimization;
- **Evaluation & Visualization**: Provides metrics like R²/RMSE, supports visualization of temperature fields/modes, etc.

## Performance: Experimental Results on 17 Million Sample Dataset

On the flow boiling dataset (17 million samples), the performance comparison of the three strategies is as follows:
| Strategy | R² | Improvement |
|---|---|---|
| Raw Temperature | Baseline | - |
| Gradient Enhancement | Moderate improvement | Medium |
| Modal Mapping | 0.729 | +69% |
The significant improvement of Model C verifies the value of physics-informed methods. By extracting physically relevant modes via POD, it more efficiently captures the intrinsic relationship between temperature and heat flux.

## Application Scenarios and Current Limitations

**Application Scenarios**:
- Experimental Thermal Fluid Dynamics: Analyze infrared data to invert heat flux;
- Energy System Optimization: Boiler/heat exchanger design and operation optimization;
- Electronic Heat Dissipation: Heat dissipation design for high-power devices;
- Chemical Processes: Optimization of phase-change heat transfer reactors.
**Limitations**:
- Dataset Dependence: The R²=0.729 result is for a specific dataset and requires independent validation;
- Model Scale: Currently uses scikit-learn MLP; ultra-large-scale data requires deep learning frameworks;
- DMD Limitation: Only applicable for short-term predictions.

## Future Directions and Project Insights

**Future Directions**:
- GPU Acceleration: Add PyTorch support for large-scale training;
- Pre-trained Models: Develop pre-trained models for specific fluids to enhance transferability;
- Improved DMD: Integrate variants like EDMD/KDMD to improve prediction accuracy.
**Insights**:
- Importance of Domain Knowledge: Physics-inspired feature engineering significantly improves performance;
- Value of Open-Source Ecosystem: Built on NumPy/SciPy, demonstrating Python's scientific computing capabilities;
- Reproducible Research: Complete implementation and documentation support result reproduction, promoting open science.