pinnse 0.0.5

A modular PyTorch framework for developing physics-informed neural network surrogate models for chemical process modeling, simulation, and process systems engineering.

pinnse: Physics-informed neural networks for process systems engineering

pinnse is a modular PyTorch framework for developing physics-informed neural-network (PINN) surrogate models for chemical process modelling, simulation, and process systems engineering. The package separates process-specific ingredients—operating bounds, governing equations, input-output formulations, and residual definitions—from reusable learning infrastructure for data handling, normalization, neural-network construction, training, validation, testing, and visualization.

The repository includes representative examples for nonideal flash separation, isothermal plug-flow reactors under multiple surrogate formulations, inverse PINNs for parameter estimation, and a nonisothermal plug-flow reactor with coupled mass and energy balances.

At its core, pinnse follows a simple principle: define the process physics once, expose it through residual functions, and train a differentiable surrogate against both labelled data and physical constraints. The resulting models are data-efficient, physically informed, and suitable for simulation, design analysis, and optimization-oriented workflows.

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Contents

• Why pinnse?
• Installation
• Quick start
• Framework workflow
• Core package
• Example case studies
• Repository structure
• Extending pinnse
• Outputs
• Citation
• License

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Why pinnse?

First-principles models in chemical engineering are often nonlinear, tightly coupled, and costly to solve repeatedly. Conventional surrogate models can reduce this cost, but purely data-driven surrogates may violate conservation laws, equilibrium relationships, or boundary conditions. pinnse addresses this limitation by combining labelled simulator or solver data with residual losses derived from governing equations.

The framework is designed for process systems engineering settings in which a single unit operation may admit multiple surrogate formulations. For example, a plug-flow reactor can be represented in terms of effluent flowrates, reaction extents, or conversions. In pinnse, these formulation-specific choices remain local to each case-study directory, while the common infrastructure for data preparation, normalization, training, and analysis is reused across systems.

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Installation

Install the latest released version from PyPI:

pip install pinnse

This installs the core runtime dependencies declared in pyproject.toml, including PyTorch, NumPy, SciPy, pandas, tqdm, scikit-learn, matplotlib, and openpyxl.

Check the installation:

python -c "from pinnse import ANN, DataModule, Training; print('pinnse is ready')"

For development or to run the example scripts, clone the repository and install it in editable mode:

git clone https://github.com/hverma99/pinnse.git
cd pinnse
pip install -e .

A clean environment is recommended:

conda create -n pinnse python=3.11 -y
conda activate pinnse
pip install pinnse

Flash data generation through Aspen Plus requires Windows, Aspen Plus, and COM automation. To install the optional Python dependency for Aspen workflows, use:

pip install "pinnse[aspen]"

Note pip install pinnse installs the Python package. To run the case-study scripts and access the supplied example datasets, clone the GitHub repository. Training from the supplied .xlsx datasets does not require Aspen Plus; Aspen is only needed when regenerating flash datasets or running Aspen-dependent workflows.

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Quick start

Use the package in Python

import torch.nn as nn
from pinnse import ANN

layer_size = [5, 64, 64, 64, 3]
model = ANN(layer_size=layer_size, activation=nn.Tanh)

Run an example from the repository

The isothermal PFR examples are the most direct entry point because they do not require Aspen Plus.

git clone https://github.com/hverma99/pinnse.git
cd pinnse/examples/isopfr/efm
python main.py
python check.py

A standard run performs the following steps:

1. loads I_S_data.xlsx and D_S_data.xlsx,
2. normalizes the input and output spaces,
3. constructs supervised, physics-collocation, and boundary-collocation loaders,
4. builds a PINN surrogate model,
5. trains against data and residual losses,
6. stores the best checkpoint in logs/, and
7. evaluates the trained model through check.py.

The nonisothermal PFR example follows the same pattern:

cd pinnse/examples/nonisopfr
python main.py
python check.py

Flash examples can be trained from the supplied datasets:

cd pinnse/examples/flash/case1
python main.py

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Framework workflow

pinnse implements a composite PINN objective of the form

\mathcal{L}_{\mathrm{total}}
= \mathcal{L}_{\mathrm{data}}
+ \lambda_P\,\mathcal{L}_{\mathrm{physics}}
+ \lambda_B\,\mathcal{L}_{\mathrm{boundary}},

where the data loss fits labelled samples, the physics loss penalizes residual violations at collocation points, and the boundary loss enforces boundary or initial conditions.

Stage	Purpose	Typical file
Framework initialization	Defines process variables, operating bounds, model formulation, architecture, dataset sizes, optimizer, scheduler, and loss weights.	main.py
Data generation and sampling	Generates labelled data from Aspen Plus, SciPy solvers, or other process models.	data_gen.py
Data loading	Splits labelled data and constructs supervised, physics-collocation, and boundary-collocation loaders.	pinnse/data.py
PINN architecture	Defines the differentiable neural-network surrogate.	pinnse/PINNs.py
Physics residual formulation	Encodes governing equations in residual form and evaluates them on collocation batches.	phys_res.py
Training, validation, and testing	Optimizes the surrogate, monitors validation performance, checkpoints the best model, and records histories.	pinnse/train.py
Post-processing and analysis	Evaluates trained models, denormalizes outputs, computes errors, and generates figures.	check.py, pinnse/utils.py, pinnse/plots.py

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Core package

The reusable framework is implemented in the pinnse/ package.

pinnse/PINNs.py

Defines neural-network architectures used as PINN surrogates.

Class	Role
ANN	Fully connected feedforward network with Xavier-uniform weight initialization and zero biases.
SANN	Feedforward network with a Softplus output layer, useful when outputs must remain non-negative.
BranchedANN	Shared-trunk architecture with multiple output heads for structured multi-output regression.
Fourier_ANN	Feedforward architecture with Fourier feature embedding for strongly varying mappings.

pinnse/data.py

Provides DataModule, which converts normalized pandas.DataFrame objects into PyTorch data loaders.

Key capabilities include:

• supervised train/validation/test splits through labeled_data_loader(),
• physics-collocation sampling through phys_colloc_loader(),
• boundary-collocation sampling through bnd_colloc_loader(),
• data-loader inspection and export utilities.

phys_colloc_loader() samples the interior input space using Latin hypercube sampling. Composition variables with prefixes such as Z_, X_, or Y_ are sampled with Dirichlet distributions so that composition groups remain physically meaningful.

pinnse/train.py

Implements the Training class, which couples supervised data loss with optional physics and boundary residual losses.

Functionality includes:

• epoch-wise training with adam_step(),
• optional second-stage optimization with lbfgs_step(),
• validation and testing through validate_test(),
• checkpointing of the best model,
• storage of training and validation loss histories,
• gradient-norm tracking for data, physics, and boundary losses,
• optional adaptive updating of loss weights.

pinnse/utils.py

Provides common utilities for normalization, denormalization, result export, and post-training analysis.

Class	Role
Normalization	Applies min-max, centred scaling, max-absolute, mean normalization, and z-score transformations. Includes routines specialized for PFR formulations.
Denormalization	Maps normalized model predictions back to dimensional variables.
Analyze	Loads trained models, evaluates predictions, and computes error metrics.
Save	Writes histories or results to Excel or CSV.

pinnse/plots.py

Defines the Plotter class for visualizing model-development history, including training and validation losses, physics and boundary weights, gradient histories, inverse-parameter trajectories, and combined summary plots.

pinnse/__init__.py

Exports the main public API:

from pinnse import (
    ANN, SANN, BranchedANN, Fourier_ANN,
    DataModule, Training, Plotter,
    Normalization, Denormalization, Save, Analyze,
)

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Example case studies

The repository includes example workflows in examples/.

Example	Description
examples/flash/case1	PINN workflow for a flash-separation formulation using supplied labelled data.
examples/flash/case2	Alternative flash formulation with its own residual and training script.
examples/isopfr/efm	Isothermal PFR using an effluent-flow formulation.
examples/isopfr/erm	Isothermal PFR using an extent-of-reaction formulation.
examples/isopfr/efm.inverse	Inverse PINN workflow for parameter estimation in the isothermal PFR setting.
examples/nonisopfr	Nonisothermal PFR with coupled mass and energy balances.

A typical example directory contains:

• main.py: model setup and training script,
• phys_res.py: physics and boundary residual definitions,
• data_gen.py: dataset generation script,
• check.py: post-training evaluation and comparison,
• I_S_data.xlsx, D_S_data.xlsx: labelled input and output datasets,
• pfr_model.py: process model helper functions where applicable,
• run.sh: convenience shell script.

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Repository structure

pinnse/
├── docs/
│   ├── logo.png
│   └── framework_overview.png
├── examples/
│   ├── flash/
│   │   ├── Aspen Simulations/
│   │   ├── case1/
│   │   └── case2/
│   ├── isopfr/
│   │   ├── efm/
│   │   ├── erm/
│   │   ├── efm.inverse/
│   │   └── cm/
│   └── nonisopfr/
├── pinnse/
│   ├── __init__.py
│   ├── PINNs.py
│   ├── data.py
│   ├── train.py
│   ├── utils.py
│   └── plots.py
├── pyproject.toml
├── README.md
└── LICENSE

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Extending pinnse

To apply pinnse to a new process system:

1. define the process inputs (I_S) and outputs (D_S),
2. generate or collect labelled datasets,
3. write a phys_res.py file that evaluates the governing-equation residuals,
4. define any required boundary residuals,
5. construct a main.py script that configures the architecture, data loaders, optimizer, scheduler, and loss weights,
6. train the model using Training, and
7. evaluate the resulting surrogate through a check.py script.

This design keeps the process-specific physics local to each case while allowing the rest of the pipeline to be inherited from the package.

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Outputs

A typical training run generates:

• a best-model checkpoint,
• training and validation loss histories,
• optional weight and gradient histories,
• prediction-versus-reference comparison results,
• figures summarizing model performance.

The exact output files are controlled by the example scripts and their logging conventions.

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Citation

If you use pinnse in academic work, please cite the associated manuscript when available. Citation details will be added here following publication.

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License

This project is released under the MIT License. See LICENSE for details.