We are pleased to announce the release of DeepFlame v2.0, marking a paradigm shift in our journey toward Agent-driven Combustion Scientific Computing. Building on over 3 years of practices in the AI+HPC+CombustionCFD methodology, this version goes beyond the traditional goal of mere computational acceleration. By integrating AI agents directly into the workflow to automate tedious coding and simulation tasks, DeepFlame v2.0 aims to liberate productivity and empower researchers to focus exclusively on scientific innovation.
DeepFlame Agent ecosystem
1. CoCo - GPU Programming Agent
CoCo is a code-migration agent for DeepFlame-GPU that understands the semantics of legacy C++ numerical algorithms and automatically generates, reviews, and tests CUDA code following the DeepFlame-GPU framework. It significantly lowers the barrier to GPU-accelerated CFD development, allowing researchers to focus on physical modeling rather than CUDA programming.
2. FlamePilot – CFD Simulation Agent for Combustion
FlamePilot is a CFD agent designed as a “digital teammate” for combustion simulations in DeepFlame. Through natural language interaction, it assists users in setting up simulations, autonomously diagnoses issues based on runtime feedback, proposes improvements, and performs corrective optimizations until convergence is achieved.
Check https://github.com/deepflame-ai/flamepilot for more information.
3. DFODE-kit Trainer - DFODE Neural Network Training Agent
DFODE-kit Trainer enables agent-driven training of combustion chemistry DNN models. Using natural language instructions, it autonomously handles operating condition setup, data generation, model training, and validation, greatly improving the efficiency and accessibility of neural chemistry model development.
Check https://github.com/deepflame-ai/DFODE-kit/blob/agent/agent_user_guide_zh.md for more information.
The future agents will be updated in deepflame-dev/agents folder.
New features
DFODE-kit - Deep Learning Package for Combustion Kinetics
DFODE-kit is an open-source Python package designed to accelerate combustion simulations by efficiently solving flame chemical kinetics governed by high-dimensional stiff ordinary differential equations (ODEs). This package integrates deep learning methodologies to replace conventional numerical integration, enabling significant speedups and improved accuracy. To train your own DNN model, try to follow DFODE-kit tutorials or DFODE-kit Trainer agent.
Other updates since v1.6.0
- Add 2D Riemann problem case by @circlexiang in #560
- update README.md by @pkuLmq in #561 #562
- Improve Initialization in dfChemistryModel by @xiao312 in #563
- CI: Change actions to self-hosted runner by @TimoLin in #557
- Bug fix: improve the logic of sorting singular values in SIGMA sgs model. by @TimoLin in #574
- Add support for the localBlended scheme by @TimoLin in #577
- Use absolute enthalpy in df0DFoam solver by @Copilot & @TimoLin in #578
New Contributors
- @circlexiang made their first contribution in #560
- @TimoLin made their first contribution in #557
- GitHub Copilot is introduced to fix simple issues like #578
Full Changelog: v1.6.0...v2.0.0
