pibert 0.1.1

Physics-Informed BERT-style Transformer for Multiscale PDE Modeling

PIBERT: Physics-Informed BERT-style Transformer

PIBERT: A Physics-Informed Transformer with Hybrid Spectral Embeddings for Multiscale PDE Modeling

Introduction

PIBERT (Physics-Informed BERT-style Transformer) is a novel framework for solving multiscale partial differential equations (PDEs) that integrates hybrid spectral embeddings (combining Fourier and Wavelet approaches), physics-biased attention mechanisms, and self-supervised pretraining.

Unlike existing approaches that only partially address the multiscale challenge, PIBERT unifies three major innovations:

• A hybrid Fourier-Wavelet embedding that captures both global structures and localized phenomena
• A physics-informed attention bias derived from PDE residuals
• A dual-task self-supervised pretraining strategy (Masked Physics Prediction & Equation Consistency Prediction)

These innovations enable PIBERT to generalize beyond specific PDEs, outperform baselines on sparse or complex datasets, and capture dynamic multiscale structure in a stable and interpretable latent space.

Key Features

• Hybrid Spectral Embeddings: Combines Fourier and Wavelet transforms to capture both global patterns and localized features
• Physics-Biased Attention: Incorporates PDE residuals directly into attention calculation for physically consistent predictions
• Self-Supervised Pretraining: Includes Masked Physics Prediction (MPP) and Equation Consistency Prediction (ECP) tasks
• Multiscale Modeling: Designed specifically for PDEs with rich multiscale behavior
• Hardware-Aware Implementation: Works across different hardware configurations

Hardware Requirements

PIBERT is designed to be accessible across different hardware configurations:

Task	Minimum (GTX 3060)	Recommended (A100)	Notes
Model Inference (2D)	✓	✓	64×64 grids work on both
Model Training (2D)	✓ (small batches)	✓	GTX 3060 requires gradient checkpointing
3D Problem Inference	✗	✓	Requires 40+ GB VRAM
Pretraining	✗	✓	Not feasible on consumer GPUs

Installation

# Basic installation
pip install pibert

# For development with testing and documentation tools
pip install pibert[dev]

# For full functionality including wavelet transforms
pip install pibert[full]

Quick Start

Verify installation with CPU (runs in <60s on any system):

from pibert import PIBERT
from pibert.utils import load_dataset

# Load a small sample dataset
dataset = load_dataset("reaction_diffusion")

# Initialize a small model
model = PIBERT(
    input_dim=1,
    hidden_dim=64,
    num_layers=2,
    num_heads=4
)

# Perform prediction
pred = model.predict(dataset["test"]["x"][:1], dataset["test"]["coords"][:1])

print(f"Prediction shape: {pred.shape}")

For more examples, see the examples directory.

Performance Comparison

PIBERT demonstrates state-of-the-art performance across multiple benchmarks:

1D Reaction Equation

Model	Relative L1	Relative L2	MAE
PINN	0.0651	0.0803	0.0581
FNO	0.0123	0.0150	0.0100
Transformer	0.0225	0.0243	0.0200
PINNsFormer	0.0065	0.0078	0.0060
PIBERT	0.0061	0.0074	0.0056

CFDBench (Cavity Flow)

Model	MSE(u)	MSE(v)	MSE(p)
PINNs	0.0500	0.0300	0.01500
Spectral PINN	0.0200	0.0045	0.00085
FNO	0.0113	0.0012	0.00021
PINNsFormer	0.0065	0.0007	0.00003
PIBERT(Lite)	0.0103	0.0011	0.000046

Ablation Study Results

The ablation study confirms the importance of each component:

Model Variant	MSE (Test)	NMSE (Test)
PIBERT (Full)	0.4975	1.3409
Fourier-only	1.6520	12.4010
Wavelet-only	0.4123	1.1021
Standard-attention	1.3201	9.8760
FNO	1.8099	13.5830
UNet	3.7006	29.2627

Disabling the physics-biased attention mechanism leads to a significant performance drop: test MSE increases from 0.4975 to 1.3201, and NMSE jumps from 1.34 to 9.88.

Reproducibility

All results in the paper can be reproduced using the provided code. The ablation studies were verified on a GTX 3060 (12GB VRAM), while the full-scale experiments used A100 GPUs. We provide configuration files for both hardware setups.

To reproduce the ablation study:

jupyter notebook examples/ablation_study_gpu.ipynb

Citing PIBERT

If you find PIBERT useful in your research, please cite our paper:

@article{chakraborty2024pibert,
  title={PIBERT: A Physics-Informed Transformer with Hybrid Spectral Embeddings for Multiscale PDE Modeling},
  author={Chakraborty, Somyajit and Xizhong, Chen},
  year={2025}
}

License

Distributed under the Apache 2.0 License. See LICENSE for more information.

Support

For support and questions, please open an issue on GitHub or contact the authors:

• Somyajit Chakraborty: [e-mail]
• Chen Xizhong: [e-mail]

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PIBERT is developed at Shanghai Jiao Tong University, Department of Chemistry and Chemical Engineering