Merge branch 'zt-dev' of github.com:deepmodeling/deepflame-dev into zt-dev

Author: TimoLin

README.md

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-DeepFlame is a deep learning empowered computational fluid dynamics package for single or multiphase, laminar or turbulent, reacting flows at all speeds. It aims to provide an open-source platform to combine the individual strengths of [OpenFOAM](https://openfoam.org), [Cantera](https://cantera.org), and [PyTorch](https://pytorch.org/) libraries for deep learning assisted reacting flow simulations. It also has the scope to leverage the next-generation heterogenous supercomputing and AI acceleration infrastructures such as GPU and FPGA.
+DeepFlame is a deep learning empowered computational fluid dynamics package for single or multiphase, laminar or turbulent, reacting flows at all speeds. It aims to provide an open-source platform to combine the individual strengths of [OpenFOAM](https://openfoam.org), [Cantera](https://cantera.org), and [PyTorch](https://pytorch.org/) libraries for deep learning assisted reacting flow simulations. It also has the scope to empower the combustion community to pursue scientific innovation by combining the power of heterogeneous acceleration (GPU/FPGA) with autonomous agent-driven workflows.
 
-The neural network models used in the tutorial examples can be found at– [AIS Square](https://www.aissquare.com/). To run DeepFlame with DNN, download the DNN model [DFODE](https://www.aissquare.com/models/detail?pageType=models&name=DF-ODENet_DNNmodel&id=197) into the case folder you would like to run. To train your own DNN models, please use the [DFODE-kit](https://github.com/deepflame-ai/DFODE-kit) package developed by the DeepFlame team.
+The neural network models used in the tutorial examples can be found at– [AIS Square](https://www.aissquare.com/). To run DeepFlame with DNN, download the DNN model [DFODE](https://www.aissquare.com/models/detail?pageType=models&name=DF-ODENet_DNNmodel&id=197) into the case folder you would like to run. To train DNN models for your specific problem, please use the [DFODE-kit](https://github.com/deepflame-ai/DFODE-kit) package developed by the DeepFlame team.
 
 ## Documentation
 Detailed guide for installation and tutorials is available on [our documentation website](https://deepflame.deepmodeling.com).
 
 ## Features
+New in v2.0 (2026/1/28):
+- Introduce Agent-driven Combustion Scientific Computing as a new research paradigm, extending DeepFlame from pure acceleration to workflow-level automation for CombustionCFD
+- Develope [FlamePilot](./agents/FlamePilot.md), a CFD simulation agent for combustion case setup, runtime diagnosis, and iterative optimization via natural language interaction
+- Introduce [DFODE-kit](https://github.com/deepflame-ai/DFODE-kit), a deep learning package for accelerating stiff, high-dimensional chemical kinetics ODEs
+- Develope [DFODE-Kit Trainer](./agents/DFODE-kit-Trainer.md), an agent-driven workflow for training combustion-chemistry neural-networks
+- Improve initialization in `dfChemistryModel`
+- Fix bug in `df0DFoam`
+- Fix bug in LES `SIGMA` sgs model
+- Add support for the localBlended scheme 
+- Change build-test actions to self-hosted runner
+
 New in v1.6 (2025/5/30):
 - Add a new solver, dfSteadyFoam, a steady-state compressible flow solver. It supports turbulence and uses the SIMPLE algorithm to efficiently compute steady solutions
 - Extend energy model to support sensible enthalpy (hs) in addition to absolute enthalpy (ha) and internal energy (ea). Furthermore, T, h and cp can now be computed independently of Cantera, improving flexibility and removing external dependencies. `CANTERA_THERMO` is added in bashrc to control method to calculate h, cp and update T. `CANTERA_THERMO=1`(current default) means using Cantera and `CANTERA_THERMO=0` means using deepflame.

agents/FlamePilot.md

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 FlamePilot
 ==========
-FlamePilot is an AI-powered CFD agent that leverages DeepFlame to assist users in setting up, running, and analyzing combustion simulations. It provides an intuitive interface for both novice and experienced users to interact with complex CFD tasks using natural language commands.
 
-To try FlamePilot, visit this Bohrium App: https://www.bohrium.com/apps/deepflame-cfd-agent.
+<div align="center">
+  <img src="https://github.com/xiao312/flamepilot-dev/raw/main/docs/assets/logo.png" alt="FlamePilot logo" width="640" />
+</div>
+
+FlamePilot is an **agentic runner for combustion CFD workflows**, designed to automate and assist simulations within the DeepFlame ecosystem. It provides a **CLI-first, tool-aware agent** that translates natural language instructions into executable CFD actions, covering case setup, solver configuration, execution, diagnostics, and iteration.
+
+FlamePilot emphasizes **executability and traceability**: it helps you go from intent → runnable case, while keeping workflows inspectable and compatible with research-grade CFD practices.
+
+To try FlamePilot, visit the <a href="https://github.com/deepflame-ai/flamepilot">project repository</a>.