🛸 Deep Space Photonics Thermal Advisor

A tool-using agent + physics simulator for recommending thermal mitigation strategies in deep space photonic instruments — runs on managed AWS Bedrock or a self-hosted, fine-tuned open-weight model

Physics simulation · Agentic tool use · Scenario retrieval · XGBoost classification · QLoRA + GGUF open-weight fine-tuning · Streamlit demo

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💡 The Problem

Photonic Integrated Circuits (PICs) are the backbone of next generation space probe instruments that operate in deep space — spectrometers, laser communication terminals, waveguide sensor arrays, and photonic signal processors. But space is brutal:

• 🌡️ Spectral drift — temperature swings shift refractive indices, pushing resonant wavelengths off-target and corrupting measurements
• 📐 Waveguide misalignment — differential thermal expansion between chip layers destroys optical coupling, killing signal throughput
• 💥 Mechanical cracking — repeated thermal cycling fatigues bonding interfaces and dielectric layers until catastrophic failure

A spectrometer on a Jovian probe faces 180 K temperature swings. An optical link in the outer solar system endures 240 K. The wrong mitigation strategy means mission failure.

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✨ The Solution

This project pairs deterministic physics with an agent that reasons over a knowledge data store — no fine-tuning required. A Bedrock foundation model decides which tools to call for any instrument-material-environment combination, then synthesizes a grounded recommendation:

Layer	What It Does	Status
🔬 Physics Simulator	Computes Δn and strain from first principles	✅ Live
🤖 Tool-Using Agent	Reasons over the scenario and calls tools via a pluggable backend	✅ Live
☁️ Bedrock Backend	Managed foundation model via the Converse API	✅ Live
🧠 Open-Weight Backend	Self-hosted Llama 3.3 / Qwen2.5 fine-tuned with QLoRA, served as GGUF	✅ Live
📚 Scenario Data Store	Retrieves similar prior cases from the 40K-scenario knowledge base	✅ Live
📊 XGBoost Classifier	Fast Passive / Active / Hybrid prediction with calibrated probabilities	✅ Live
🖥️ Streamlit App	Interactive two-mode demo (physics + agentic advisor)	✅ Live
🧪 CI Pipeline	Automated pytest across Python 3.10–3.12 on every push and PR	✅ Live

Knowledge vs. behavior — two different jobs

• Knowledge stays out of the weights. The data store holds the facts; updating it means re-indexing, not re-training. Every recommendation cites real simulator output, classifier probabilities, and retrieved scenarios.
• Behavior can be baked into the weights — for open-weight models. You don't fine-tune to teach facts; you fine-tune so an open-weight model reliably emits this stack's tool calls without babysitting it with a giant system prompt. See Open-Weight Fine-Tuning.
• Composable — the simulator, classifier, and data store are independent tools the model orchestrates on demand, regardless of backend.

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🏗️ Architecture

Runtime — agentic tool-use loop

                 ┌──────────────────────────────────────┐
                 │        Streamlit Interactive App      │
                 │   (Physics Simulator · Agentic Advisor)│
                 └──────────────────────────────────────┘
                                   │
                                   ▼
                 ┌──────────────────────────────────────┐
                 │              ThermalAgent             │
                 │       reason → act tool-use loop      │
                 └──────────────────────────────────────┘
                                   │
                   ┌───────────────┴───────────────┐
                   ▼                               ▼
        ┌────────────────────┐         ┌────────────────────────┐
        │   Bedrock backend  │         │      Local backend     │
        │   (managed FM via  │         │  (fine-tuned GGUF via  │
        │    Converse API)   │         │ OpenAI-compatible EP)  │
        └────────────────────┘         └────────────────────────┘
                   └───────────────┬───────────────┘
                                   ▼
                 ┌──────────────────────────────────────┐
                 │             ToolDispatcher            │
                 └──────────────────────────────────────┘
                     │              │               │
                     ▼              ▼               ▼
            ┌──────────────┐ ┌──────────────┐ ┌──────────────────┐
            │  simulate_   │ │  classify_   │ │ search_thermal_  │
            │  thermal_    │ │  strategy    │ │ knowledge        │
            │  drift       │ │  (XGBoost)   │ │ (data store)     │
            └──────────────┘ └──────────────┘ └──────────────────┘
                     │              │               │
                     ▼              ▼               ▼
            ┌──────────────┐ ┌──────────────┐ ┌──────────────────┐
            │ ThermalDrift │ │  Strategy    │ │ ThermalDataStore │
            │  Simulator   │ │  Classifier  │ │ TF-IDF · 40K rows│
            └──────────────┘ └──────────────┘ └──────────────────┘

The agent runs a tool-use loop: the model requests a tool, the ToolDispatcher executes it against the simulator, classifier, or data store, the result is fed back, and the loop repeats until the model returns a final recommendation. The model backend is pluggable — the same loop runs against managed Bedrock or a self-hosted fine-tuned open-weight model (the LocalToolBackend translates Bedrock Converse ↔ OpenAI tool-calling). The data store is likewise backend-agnostic — the local TF-IDF index can be swapped for Amazon Bedrock Knowledge Bases in production without changing the agent or tools.

Offline — open-weight fine-tuning pipeline (feeds the Local backend)

   HuggingFace          training_data.py         finetune.py        quantize.py
   40K dataset    ──▶    SFT trace builder   ──▶  QLoRA 4-bit   ──▶  merge + GGUF   ──▶  GGUF
                        (real Strategy            (LoRA adapter)     (Q4_K_M)            served by
                         Classifier +                                                    Local backend
                         ThermalDataStore →
                         grounded tool traces)

The SFT builder runs the real StrategyClassifier and ThermalDataStore to ground each training trace in genuine tool outputs (with synthesized fallbacks for unseen inputs), so the fine-tuned open-weight model learns this stack's exact tool-calling dialect — not synthetic proxies.

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📦 Dataset

40,000 synthetic thermal scenarios — Taylor658/deep-space-optical-chip-thermal-dataset

The dataset is the agent's knowledge store: scenarios are indexed for retrieval and used to train the XGBoost classifier.

Chip Materials

Material	dn/dT (K⁻¹)	α — Thermal Expansion (K⁻¹)	Sensitivity
Silicon	1.86 × 10⁻⁴	2.6 × 10⁻⁶	High
Silicon Nitride	2.45 × 10⁻⁵	8.0 × 10⁻⁷	Low
Polymer	1.1 × 10⁻⁴	2.2 × 10⁻⁶	Moderate
Indium Phosphide	3.4 × 10⁻⁴	4.6 × 10⁻⁶	Very High

Environments

Environment	Expected ΔT (K)	Severity
Near Earth Deep Space	120	⚠️ Moderate
Mars Transit	150	⚠️ Moderate
Jovian System	180	🔴 High
Outer Solar System	240	🔴 Critical

Coverage

• 4 instruments — Spectrometer, Laser Communication Terminal, Waveguide Sensor Array, Photonic Signal Processor
• 3 strategy types — Passive, Active, Hybrid

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🚀 Quick Start

# Clone
git clone https://github.com/ATaylorAerospace/Thermal-Agent.git
cd Thermal-Agent

# Install
pip install -r requirements.txt

# Configure
cp .env.example .env
# → Edit .env with your AWS credentials (Bedrock access)

# Build the agent's knowledge artifacts:
#   - scenario data store (vector index)
#   - XGBoost strategy classifier
bash scripts/build_index.sh

# Run the interactive app
streamlit run app/streamlit_app.py

---

🔮 Usage Examples

1. Physics Simulation — Compute Thermal Risk

from src.simulator import ThermalDriftSimulator

sim = ThermalDriftSimulator()

# Evaluate Indium Phosphide on a Jovian mission
result = sim.evaluate("Indium Phosphide", "Jovian System")

print(f"Δn = {result['delta_n']:.6f}")       # Δn = 0.061200
print(f"Strain = {result['strain']:.2e}")     # Strain = 8.28e-04
print(f"Risk: {result['risk']}")              # Risk: Critical
print(f"Strategy: {result['recommended_strategy_hint']}")  # Strategy: Hybrid

2. Scenario Retrieval — Query the Data Store

from src.datastore import ThermalDataStore

store = ThermalDataStore.from_huggingface()  # or .load("results/thermal_datastore.pkl")

for hit in store.query("Indium Phosphide spectrometer Jovian spectral drift", top_k=3):
    print(f"{hit['similarity']:.3f}  {hit['instrument']} → {hit.get('strategy_type')}")

3. XGBoost Strategy Prediction

from src.strategy_classifier import StrategyClassifier

clf = StrategyClassifier()
clf.load("results/strategy_classifier.pkl")

proba = clf.predict_proba(
    material="Silicon",
    instrument="Spectrometer",
    environment="Mars Transit",
    thermal_effect="Spectral Drift",
)
print(proba)
# {'Active': 0.12, 'Hybrid': 0.61, 'Passive': 0.27}

4. The Agent — Tool-Grounded Recommendation

from src.agent import ThermalAgent

# Loads the data store and classifier from config/agent_config.yaml if present
agent = ThermalAgent.from_config()

result = agent.run(
    "Instrument: Laser Communication Terminal\n"
    "Material: Indium Phosphide\n"
    "Environment: Outer Solar System\n"
    "Thermal Effect: Waveguide Misalignment\n"
    "What thermal mitigation strategy should be used and why?"
)

print(result["answer"])        # the grounded recommendation
print(result["tool_calls"])    # every tool the agent invoked, with inputs + results

5. Build the Knowledge Artifacts

# One command — build the data store index and train the classifier
bash scripts/build_index.sh

---

🧠 Open-Weight Fine-Tuning (QLoRA → GGUF)

Run the same agent against a self-hosted, fine-tuned open-weight model instead of Bedrock. The point isn't to teach the model facts (the data store does that) — it's to make a raw open-weight model a reliable, low-overhead tool-caller for this exact stack:

• Close the out-of-the-box gap — off-the-shelf Llama 3.3 / Qwen2.5 are capable but loose at strict tool-call formatting. QLoRA bakes the call format in.
• Teach your dialect — the SFT traces encode these tool schemas and call patterns, so the model speaks your infrastructure natively.
• Own your unit economics — with the behavior in the weights you can strip the giant tool-description system prompt, cutting input-token overhead per inference.

Pipeline

# Heavy deps on a GPU host (kept out of core requirements / CI)
pip install -r requirements-finetune.txt

# 1) Build agentic tool-calling SFT data  2) QLoRA fine-tune  3) merge + GGUF quantize
bash scripts/finetune_pipeline.sh

Stage	Module	Output
Build SFT traces	src/training_data.py	data/finetune/{train,validation}.jsonl
QLoRA fine-tune	src/finetune.py	LoRA adapter in results/thermal-agent-lora/
Merge + quantize	src/quantize.py	results/thermal-agent.Q4_K_M.gguf

Base model, LoRA rank, 4-bit quantization, and the GGUF quant type are all set in config/finetune_config.yaml (defaults: Llama 3.3 70B, nf4 4-bit, Q4_K_M).

Serve and switch the agent to it

Serve the GGUF behind any OpenAI-compatible endpoint (llama.cpp server, Ollama, or vLLM), then flip the provider in config/agent_config.yaml:

agent:
  provider: local        # bedrock | local
local:
  model: thermal-agent
  base_url: http://localhost:8000/v1

ThermalAgent.from_config() now routes through LocalToolBackend — the rest of the agent loop is unchanged.

---

📁 Repository Structure

Thermal-Agent/
├── .github/
│   └── workflows/
│       └── ci.yml                   # GitHub Actions CI — pytest on 3.10–3.12
├── app/
│   └── streamlit_app.py             # Interactive two-tab demo
├── config/
│   ├── agent_config.yaml            # Agent provider, data store, classifier paths
│   └── finetune_config.yaml         # QLoRA + GGUF fine-tuning settings
├── docs/
│   └── thermals.png                 # Hero banner image
├── notebooks/
│   ├── 01_eda.ipynb                 # Exploratory data analysis
│   ├── 02_agent_walkthrough.ipynb   # End-to-end agent walkthrough
│   └── 03_open_weight_finetuning.ipynb  # QLoRA → GGUF walkthrough
├── results/                         # Model, index & adapter artifacts (gitignored)
├── scripts/
│   ├── build_index.sh               # Build data store + train classifier
│   └── finetune_pipeline.sh         # Build SFT data → QLoRA → GGUF
├── src/
│   ├── __init__.py                  # Public API exports
│   ├── agent.py                     # Tool-use agent (pluggable backend)
│   ├── backends.py                  # Local open-weight (OpenAI-compatible) backend
│   ├── tools.py                     # Tool specs + dispatcher
│   ├── datastore.py                 # Scenario retrieval (knowledge store)
│   ├── training_data.py             # Agentic SFT data builder
│   ├── finetune.py                  # QLoRA fine-tuning (Llama 3.3 / Qwen2.5)
│   ├── quantize.py                  # Adapter merge + GGUF quantization
│   ├── simulator.py                 # Physics-based thermal drift engine
│   └── strategy_classifier.py       # XGBoost Passive/Active/Hybrid
├── tests/
│   ├── __init__.py                  # Test package init
│   ├── test_agent.py                # Agent loop tests (mocked backend)
│   ├── test_backends.py             # Local backend translation tests
│   ├── test_tools.py                # Tool dispatch tests
│   ├── test_datastore.py            # Data store retrieval tests
│   ├── test_training_data.py        # SFT data builder tests
│   ├── test_classifier.py           # Classifier tests
│   └── test_simulator.py            # Physics simulator tests
├── .env.example                     # Credential / endpoint template
├── .gitignore
├── conftest.py                      # Pytest path configuration
├── CONTRIBUTING.md                  # Development setup & PR guidelines
├── README.md
├── requirements.txt                 # Core dependencies (agent + tests)
└── requirements-finetune.txt        # Heavy, optional fine-tuning dependencies

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🧩 Components

🔬 Physics Simulator (src/simulator.py)

Computes refractive index shift (Δn = dn/dT × ΔT) and mechanical strain (ε = α × ΔT) for any material-environment pair. Classifies risk as Low → Moderate → High → Critical and maps to a strategy hint. Exposed to the agent as the simulate_thermal_drift tool.

📚 Scenario Data Store (src/datastore.py)

Indexes the 40K HuggingFace scenarios with TF-IDF and retrieves the most similar prior cases by cosine similarity. Backend-agnostic — swappable for Amazon Bedrock Knowledge Bases in production. Exposed as the search_thermal_knowledge tool.

🛠️ Agent Tools (src/tools.py)

Bedrock Converse tool specifications plus a ToolDispatcher that routes each tool call to the simulator, classifier, or data store. Tools backed by a missing artifact degrade gracefully.

🤖 Thermal Agent (src/agent.py)

A tool-using agent that runs a reason-act loop — requesting tools, feeding results back, and iterating — until it returns a grounded recommendation along with the full trace of tool calls. The model backend is pluggable (provider: bedrock | local).

🧠 Open-Weight Backend & Fine-Tuning (src/backends.py, src/training_data.py, src/finetune.py, src/quantize.py)

LocalToolBackend runs a self-hosted, QLoRA-fine-tuned Llama 3.3 / Qwen2.5 model (exported to GGUF) behind an OpenAI-compatible endpoint, translating Bedrock Converse ↔ OpenAI tool-calling so it drops straight into the agent loop. The fine-tuning trio builds agentic SFT traces, runs 4-bit QLoRA, and merges + quantizes the adapter to GGUF.

📊 Strategy Classifier (src/strategy_classifier.py)

XGBoost classifier predicting Passive / Active / Hybrid strategies with calibrated probability estimates. Exposed as the classify_strategy tool and usable standalone.

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🧪 Testing

Tests run automatically via GitHub Actions CI on every push and pull request against Python 3.10, 3.11, and 3.12.

# Run all tests locally
pytest tests/ -v

# Run a single suite
pytest tests/test_agent.py -v

The agent tests use a scripted fake Bedrock client, so the full tool-use loop is exercised without any AWS calls.

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🤝 Contributing

See CONTRIBUTING.md for development setup, testing instructions, and pull request guidelines.

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📜 License

This work is licensed under CC BY 4.0.

Copyright (c) 2026 A Taylor

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📬 Contact

Have questions, ideas, or want to collaborate? Reach out directly: