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Nonlinearity encoding to improve extrapolation capabilities for unobserved physical states.

Gyoung S Na¹, Seunghun Jang¹, Hyunju Chang¹

¹Korea Research Institute of Chemical Technology (KRICT), Republic of Korea. ngs0@krict.re.kr.

Physical Chemistry Chemical Physics : PCCP

|January 4, 2022

Summary

This summary is machine-generated.

This study introduces a new machine learning method to improve predictions for physical systems. The novel data-driven encoder significantly reduces extrapolation errors in complex scientific models.

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Area of Science:

Physics
Materials Science
Computational Science

Background:

Machine learning (ML) in physical science aims to predict properties of unobserved states.
Accurate prediction for data outside training distributions is challenging due to system nonlinearities.

Purpose of the Study:

To develop a data-driven method for enhancing ML extrapolation accuracy in physical systems.
To encode nonlinearities of physical systems into input representations for improved predictions.

Main Methods:

Proposed a novel data-driven encoder to represent physical systems.
Encoded system nonlinearities into input representations, creating linear-like functions.
Applied the encoder to benchmark physical science problems.

Main Results:

Significantly reduced extrapolation errors by 48.39% in the n-body problem.
Achieved a 40.04% reduction in extrapolation errors for materials property prediction.
Demonstrated the effectiveness of the proposed encoder for improving ML model extrapolation.

Conclusions:

The proposed encoder effectively handles nonlinearities in physical systems.
The method significantly enhances the extrapolation capabilities of machine learning algorithms.
This approach offers a pathway to more reliable ML predictions in physical sciences.