Code and dataset for DeepThermo-Meta-assisted autonomous design of dynamic thermal meta-regulators
Abstract
Code and dataset for DeepThermo-Meta-assisted inverse design of dynamic meta-regulators Description System requirements Dependencies Python 3.11 PyTorch 2.5.1+cu121 torchvision 0.20.1+cu121 Pydantic >= 2.6, < 3 Loguru >= 0.7, < 1 FastAPI >= 0.115, < 1 Uvicorn >= 0.34, < 1 Ollama Python package >= 0.5, < 1 Tested environments DeepThermo-Meta Agent: Windows, Python 3.11, CPU devices Ollama with the qwen3.6 local model tag (optional; model not included in this archive) Inverse-design computation: WSL2 Ubuntu 20.04, Python 3.11, PyTorch 2.5.1+cu121, CUDA 12.1, NVIDIA GeForce RTX 4070 Ti MATLAB: MATLAB (version compatible with RETICOLO V10) - RETICOLO V10 (third-party MATLAB package for RCWA; required for patterned-structure optical simulations and not included in this archive) Other software CST Studio Suite 2025 and a compatible MATLAB environment for joint simulation and validation screening Installation Miniconda conda create -n deepthermo-meta python=3.11 conda activate deepthermo-meta python -m pip install torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cu121 DeepThermo-Meta Agent installation: cd agent python -m pip install -r requirements.txt DeepThermo-Meta Agent operation Path: agent 1. Start the Web Interface python -m uvicorn app.webapp.backend.main:app --host 127.0.0.1 --port 8000 Open http://127.0.0.1:8000 in a Web browser 2. Environment Selection - Selects Arbitrary spectral selection, Temperature resistance, Mechanical flexibility, Corrosion resistance, or Space survivability before entering the design request - Locks the corresponding material preset for an environment-constrained request - Uses preset 3 (colorful) for DMR-1 to DMR-12 under Arbitrary spectral selection when no material is specified 3. Requirement Analysis and Clarification - Parses the natural-language request and extracts the visible and infrared spectral objectives - Requests only the unresolved spectral information and accepts free-form user answers 4. DMR and Material Mapping - Maps the confirmed spectral requirement to one of twelve DMR tasks - Confirms the environment-authorized or project-approved material preset 5. Downstream Input - Generates a structured DMR and material-preset contract for the inverse-design backend - The publication Web interface reports the resolved contract without automatically starting the scientific backend Command-line demonstration (optional) python -m app.interactive_clarify_main "Use DMR-10." --environment arbitrary To enable the optional local LLM, set PUBLICATION_USE_OLLAMA=true and OLLAMA_MODEL=qwen3.6 before running the Agent. Instructions and demo Dataset collection (~15 mins per model) 1. Generate Turing Pattern Database Run TULINpattern.m in MATLAB 2. DMR Generation and Spectral Analysis Run DMR_runing.m in MATLAB - Generates DMR structure modes - Obtains IR spectral responses 3. Optical Analysis Run RCWA_optical_simulation.m in MATLAB with RETICOLO V10 installed. - Calculates reflectivity and transmittance of patterned DMR structures 4. Data Integration and Preprocessing Run data_integrate.m in MATLAB - Integrates DMR results - Normalizes spectral data - Exports processed data for further analysis Dimensional reduction (~1 hour) Path: Dimensional reduction 1. Spectral Data Dimensionality Reduction Run AE_DR.py in Python - Compresses high-dimensional IR spectral data into a low-dimensional latent representation 2. Graphic Parameter Data Dimensionality Reduction Run VAE-main2.py in Python - Compresses high-dimensional DMR graphic parameters into a low-dimensional latent vector that follows a probabilistic distribution This combined latent representation serves as the input for subsequent MCGAN training and generation. Prediction (~1 hour) Path: Prediction Run Simulator_VIS_ref.py - Trains and saves the visible reflectivity prediction module. Run Simulator_VIS_trans.py - Trains and saves the visible transmittance prediction module. Run Simulator_IR.py - Trains and saves the IR emissivity prediction module. Inverse design (~8 hours) Path: MCGAN Run General_GAN_con_VIS_IR.py - Loads the concatenated low-dimensional data and pre-trained prediction modules (Simulators) - Trains the Multi-Conditional Generative Adversarial Network (MCGAN) - Uses TensorBoard to monitor training progress - Saves the trained generator and discriminator models Run General_GAN_con_VIS_IR_test.py - Loads the trained generator model - Performs inverse design for specific targets (DMR-1 to DMR-12) - Outputs the designed DMR parameters and their predicted optical performance Simulation and Validation Path: Validation Run the supplied MATLAB validation scripts - Screens inverse-designed DMR candidates - Performs RCWA optical analysis of patterned DMR structures - Uses CST Studio Suite for high-fidelity electromagnetic validation when the licensed local environment is configured
