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Related Experiment Video

Updated: May 14, 2026

Assembly, Loading, and Alignment of an Analytical Ultracentrifuge Sample Cell

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Published on: November 5, 2009

SedNet: A physics-informed operator-learning framework for rapid sedimentation velocity analytical

Andrew Basalla¹, Krishna Kumar², Zhengrong Cui¹

¹Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin 78712, USA.

International Journal of Pharmaceutics

|May 12, 2026

Summary

This study introduces SedNet, a novel machine learning framework that rapidly analyzes sedimentation velocity analytical ultracentrifugation (SV-AUC) data. SedNet utilizes physics-informed neural networks to solve the Lamm equation, offering a faster alternative to traditional methods.

Keywords:

Biopharmaceutical characterization Lamm equation Operator learning PI-DeepONet Physics-informed neural networks Scientific machine learning Sedimentation velocity analytical ultracentrifugation

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Area of Science:

Biophysics
Analytical Chemistry
Computational Biology

Background:

Sedimentation velocity analytical ultracentrifugation (SV-AUC) data analysis traditionally relies on slow numerical solvers for the Lamm equation.
Existing methods require repeated solutions, limiting experimental throughput and data interpretation speed.

Purpose of the Study:

To develop a faster and more efficient method for analyzing SV-AUC data.
To leverage machine learning, specifically physics-informed neural networks, to solve the Lamm equation.

Main Methods:

Implemented machine learning's automatic differentiation within an inverse physics-informed neural network (PINN) to solve the Lamm equation.
Developed a physics-informed deep operator network (PI-DeepONet) combined with a multi-layer perceptron (MLP) to create an end-to-end framework named SedNet.
Enforced the Lamm equation via a physics-informed forward operator within the SedNet framework.

Main Results:

Demonstrated accurate solutions to the Lamm equation using automatic differentiation and PINNs.
Successfully generated synthetic SV-AUC data and inverted it into size distributions using the PI-DeepONet and MLP combination.
Showcased SedNet's ability to analyze real SV-AUC experimental data, confirming its generalization capabilities.

Conclusions:

SedNet provides a significantly faster and accurate approach for SV-AUC data analysis compared to traditional numerical methods.
The framework's ability to generate and invert data makes it a versatile tool for biophysical characterization.
SedNet shows strong potential as a widely applicable analytical tool in scientific research.