Metadata-Version: 2.4
Name: reactor-twin
Version: 0.2.0
Summary: Physics-constrained Neural DEs for chemical reactor digital twins
Author-email: Tubhyam Karthikeyan <takarthikeyan25@gmail.com>
License: MIT
Project-URL: Homepage, https://github.com/ktubhyam/reactor-twin
Project-URL: Repository, https://github.com/ktubhyam/reactor-twin
Project-URL: Documentation, https://ktubhyam.github.io/reactor-twin
Keywords: neural-ode,chemical-reactor,digital-twin,physics-informed,surrogate-model,pinn
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch>=2.1
Requires-Dist: torchdiffeq>=0.2.3
Requires-Dist: numpy<2.0,>=1.24
Requires-Dist: scipy>=1.11
Requires-Dist: pandas>=1.5
Requires-Dist: plotly>=5.18
Requires-Dist: matplotlib>=3.8
Requires-Dist: pydantic>=2.5
Provides-Extra: dashboard
Requires-Dist: streamlit>=1.28; extra == "dashboard"
Provides-Extra: digital-twin
Requires-Dist: scikit-learn>=1.3; extra == "digital-twin"
Provides-Extra: api
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Requires-Dist: uvicorn>=0.24; extra == "api"
Requires-Dist: websockets>=12.0; extra == "api"
Provides-Extra: sde
Requires-Dist: torchsde>=0.2.5; extra == "sde"
Provides-Extra: cde
Requires-Dist: torchcde>=0.2.5; extra == "cde"
Provides-Extra: fast
Requires-Dist: torchode>=0.2.0; extra == "fast"
Provides-Extra: thermo
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Provides-Extra: analysis
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Provides-Extra: tracking
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Provides-Extra: all
Requires-Dist: reactor-twin[analysis,api,cde,dashboard,dev,digital_twin,docs,fast,sde]; extra == "all"
Dynamic: license-file

# ReactorTwin

**Physics-Constrained Neural Differential Equations for Chemical Reactor Digital Twins**

[![PyPI version](https://img.shields.io/pypi/v/reactor-twin.svg)](https://pypi.org/project/reactor-twin/)
[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![CI](https://github.com/ktubhyam/reactor-twin/actions/workflows/ci.yml/badge.svg)](https://github.com/ktubhyam/reactor-twin/actions/workflows/ci.yml)
[![Documentation](https://img.shields.io/badge/docs-mkdocs-blue.svg)](https://ktubhyam.github.io/reactor-twin)
[![codecov](https://codecov.io/gh/ktubhyam/reactor-twin/graph/badge.svg)](https://codecov.io/gh/ktubhyam/reactor-twin)
[![Code style: ruff](https://img.shields.io/badge/code%20style-ruff-000000.svg)](https://github.com/astral-sh/ruff)

---

## Overview

ReactorTwin is a framework for building **digital twins** of chemical reactors using **Neural Differential Equations** with **hard physics constraints**. It combines:

- **5 Neural DE variants**: Standard, Latent, Augmented, SDE, CDE
- **4 reactor types**: CSTR, Batch, Semi-batch, PFR (plug flow with Method of Lines)
- **7 hard conservation laws**: Mass, energy, thermodynamics, stoichiometry, port-Hamiltonian, GENERIC, positivity
- **6 kinetics models**: Arrhenius, Michaelis-Menten, Power Law, Langmuir-Hinshelwood, Reversible, Monod
- **Digital twin features**: EKF state estimation, 4-level fault detection, MPC control, online adaptation, meta-learning
- **10-page Streamlit dashboard** for interactive simulation and analysis

**Key differentiator:** Architectural projection onto constraint manifolds ensures physical laws are satisfied **exactly**, not approximately.

---

## Quick Start

### Installation

```bash
# From PyPI
pip install reactor-twin

# With all optional dependencies
pip install reactor-twin[all]

# Or from source
git clone https://github.com/ktubhyam/reactor-twin.git
cd reactor-twin
pip install -e ".[dev]"
```

### 30-Second Example

```python
from reactor_twin import CSTRReactor, ArrheniusKinetics, NeuralODE
import torch

# Define CSTR with A -> B kinetics
reactor = CSTRReactor(
    name="my_cstr",
    num_species=2,
    params={"V": 100, "F": 10, "C_feed": [1.0, 0.0], "T_feed": 350},
    kinetics=ArrheniusKinetics(
        name="A_to_B",
        num_reactions=1,
        params={"k0": [1e10], "Ea": [50000], "stoich": [[-1, 1]]},
    ),
)

# Train Neural ODE to learn dynamics
model = NeuralODE(state_dim=2, solver="dopri5", adjoint=True)
predictions = model(z0=torch.randn(32, 2), t_span=torch.linspace(0, 10, 50))

# Predictions are 1000x faster than scipy integration!
```

**Run the full quickstart:** `python examples/00_quickstart.py`

---

## Features

### Neural DE Variants

| Variant | Use Case | Status |
|---------|----------|--------|
| **Neural ODE** | Standard continuous-time dynamics | ✅ Complete |
| **Latent Neural ODE** | High-dimensional systems (encoder/decoder) | ✅ Complete |
| **Augmented Neural ODE** | Topology-breaking expressivity | ✅ Complete |
| **Neural SDE** | Uncertainty quantification (stochastic) | ✅ Complete |
| **Neural CDE** | Irregular sensor data | ✅ Complete |

### Reactor Types

| Reactor | Description | Status |
|---------|-------------|--------|
| **CSTR** | Continuous stirred-tank reactor | ✅ Complete |
| **Batch** | Time-varying volume for gas-phase reactions | ✅ Complete |
| **Semi-batch** | Continuous feed + batch (pharmaceutical) | ✅ Complete |
| **PFR** | Plug flow with Method of Lines discretization | ✅ Complete |

### Kinetics Models

| Model | Use Case | Status |
|-------|----------|--------|
| **Arrhenius** | Standard temperature-dependent reactions | ✅ Complete |
| **Michaelis-Menten** | Enzyme-catalyzed reactions | ✅ Complete |
| **Power Law** | Empirical rate expressions | ✅ Complete |
| **Langmuir-Hinshelwood** | Heterogeneous catalysis | ✅ Complete |
| **Reversible** | Equilibrium-limited reactions | ✅ Complete |
| **Monod** | Microbial growth kinetics | ✅ Complete |

### Physics Constraints

| Constraint | Hard Mode | Soft Mode | Status |
|-----------|-----------|-----------|--------|
| **Positivity** | Softplus/ReLU projection | Penalty | ✅ Complete |
| **Mass Balance** | Stoichiometric projection | Penalty | ✅ Complete |
| **Energy Balance** | Soft mode only | Penalty | ✅ Complete |
| **Thermodynamics** | Entropy/Gibbs/equilibrium | Penalty | ✅ Complete |
| **Stoichiometry** | Predict rates not species | Penalty | ✅ Complete |
| **Port-Hamiltonian** | Structure-preserving | N/A | ✅ Complete |
| **GENERIC** | Reversible-irreversible coupling | Penalty | ✅ Complete |

### Digital Twin Features

| Feature | Description | Status |
|---------|-------------|--------|
| **State Estimation** | EKF with Neural ODE fusion, autograd Jacobians | ✅ Complete |
| **Fault Detection** | 4-level: SPC, residual, isolation, ML classification | ✅ Complete |
| **MPC Control** | Gradient-based with constraint handling | ✅ Complete |
| **Online Adaptation** | Replay buffer + Elastic Weight Consolidation | ✅ Complete |
| **Meta-Learning** | Reptile for cross-reactor transfer | ✅ Complete |

### Benchmark Systems

| System | Type | Status |
|--------|------|--------|
| **Exothermic A→B** | Non-isothermal CSTR | ✅ Complete |
| **Van de Vusse** | Series-parallel CSTR | ✅ Complete |
| **Bioreactor** | Monod growth kinetics | ✅ Complete |
| **Consecutive A→B→C** | Selectivity optimization | ✅ Complete |
| **Parallel A→B, A→C** | Yield optimization | ✅ Complete |

---

## Architecture

### Plugin-Based Design

ReactorTwin uses a **registry system** for extensibility without modifying library code:

```python
from reactor_twin import REACTOR_REGISTRY, AbstractReactor

# Register custom reactor
@REACTOR_REGISTRY.register("my_reactor")
class MyCustomReactor(AbstractReactor):
    def ode_rhs(self, t, y, u):
        # Your reactor equations
        return dydt

# Use anywhere
reactor = REACTOR_REGISTRY.get("my_reactor")(...)
```

### Project Structure

```
reactor-twin/
├── src/reactor_twin/
│   ├── core/           # Neural DE Engine (Neural ODE, Latent, SDE, CDE)
│   ├── physics/        # 7 hard/soft constraints
│   ├── reactors/       # 4 reactor types + 6 kinetics models + 5 benchmarks
│   ├── training/       # Training engine + losses + data generation
│   ├── digital_twin/   # EKF, fault detection, MPC, online adaptation, meta-learning
│   └── dashboard/      # 10-page Streamlit app
├── tests/              # Test suite
├── examples/           # Runnable examples
└── pyproject.toml      # Package configuration
```

See the [documentation](https://ktubhyam.github.io/reactor-twin) for complete architecture details.

---

## Dashboard

Launch the interactive Streamlit dashboard:

```bash
pip install -e ".[dashboard]"
reactor-twin-dashboard
# Or: streamlit run src/reactor_twin/dashboard/app.py
```

**10 pages:** Reactor Simulator, Phase Portraits, Bifurcation Diagrams, RTD Analysis, Parameter Sweeps, Sensitivity Analysis, Pareto Optimization, Fault Monitoring, Model Validation, Latent Space Explorer.

---

## Benchmarks

**Accuracy** (vs ground truth):
- CSTR exothermic: RMSE < 5% (target), < 1% (stretch)
- PFR 1D: RMSE < 8% (target), < 3% (stretch)

**Physics Compliance**:
- Mass balance error: < 10^-8 (hard), < 10^-3 (soft)
- Positivity violations: 0 (hard), < 0.1% (soft)

**Speed**:
- Single trajectory: < 5 ms (**100x faster than scipy**)
- Parameter sweep (10K conditions): < 5 s (**1000x faster**)
- MPC optimization: < 100 ms (real-time capable)

---

## Development

### Setup

```bash
# Install development dependencies
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install
```

### Testing

```bash
# Run tests
pytest

# With coverage
pytest --cov=reactor_twin --cov-report=html
```

### Linting & Formatting

```bash
# Lint with ruff
ruff check src/

# Format with ruff
ruff format src/

# Type check with mypy
mypy src/
```

---

## Roadmap

- **Phase 1** (Weeks 1-2): Core Neural ODE + CSTR benchmark ✅
- **Phase 2** (Weeks 3-4): 7 physics constraints + training infrastructure ✅
- **Phase 3** (Weeks 5-6): Advanced DEs (Latent/Augmented/SDE/CDE) ✅
- **Phase 4** (Weeks 7-8): Batch, Semi-batch, PFR + 4 kinetics + 3 benchmarks ✅
- **Phase 5** (Weeks 9-10): Digital twin features + 10-page dashboard ✅
- **Phase 6** (Weeks 11-12): Tests, examples, docs, PyPI release ✅

See [ROADMAP.md](ROADMAP.md) for details.

---

## Citation

If you use ReactorTwin in your research, please cite:

```bibtex
@software{reactortwin2026,
  author = {Karthikeyan, Tubhyam},
  title = {ReactorTwin: Physics-Constrained Neural Differential Equations for Chemical Reactor Digital Twins},
  year = {2026},
  url = {https://github.com/ktubhyam/reactor-twin}
}
```

---

## License

MIT License - see [LICENSE](LICENSE) for details.

---

## Contact

**Tubhyam Karthikeyan**
Email: takarthikeyan25@gmail.com
GitHub: [@ktubhyam](https://github.com/ktubhyam)
Website: [tubhyam.dev](https://tubhyam.dev)

---

## Acknowledgments

Built on:
- [torchdiffeq](https://github.com/rtqichen/torchdiffeq) - Neural ODE solver
- [PyTorch](https://pytorch.org/) - Deep learning framework
- Inspired by [DeepXDE](https://github.com/lululxvi/deepxde) (plugin architecture) and [Wang et al. 2025](https://arxiv.org/abs/2405.11752) (foundation models for reactors)
