What type of study is this?

This is a Quantitative Study study.

October 20, 2025Open Access

When Network Architecture Meets Physics: Deep Operator Learning for Coupled Multiphysics

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Authors

KKKazuma Kobayashi JPJaewan Park QLQibang Liu

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Overview

This analysis reveals how network architecture impacts performance in multiphysics systems, suggesting optimal designs for different coupling strengths.

Key Points

Architectural design in deep operator networks significantly influences model performance in multiphysics applications.
The study finds that single-branch networks outperform multi-branch counterparts in strongly coupled multiphysics settings.
Benchmarking demonstrates surrogates can predict outcomes up to 1.8e4 times faster than traditional finite-element methods without losing accuracy.
Evaluation included a variety of complex physical problems, highlighting the importance of aligning architecture with physical coupling.

Cite This Study

Kobayashi et al. (2025) studied this question.

synapsesocial.com/papers/68f5fcdc8d54a28a75cf25d7 https://doi.org/10.48550/arxiv.2507.03660

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October 20, 2025Open Access

When Network Architecture Meets Physics: Deep Operator Learning for Coupled Multiphysics

View Full Paper

Ask AI

Bookmark

Authors

KKKazuma Kobayashi JPJaewan Park QLQibang Liu

Discussion

Member takes

Overview

This analysis reveals how network architecture impacts performance in multiphysics systems, suggesting optimal designs for different coupling strengths.

Key Points

Architectural design in deep operator networks significantly influences model performance in multiphysics applications.
The study finds that single-branch networks outperform multi-branch counterparts in strongly coupled multiphysics settings.
Benchmarking demonstrates surrogates can predict outcomes up to 1.8e4 times faster than traditional finite-element methods without losing accuracy.
Evaluation included a variety of complex physical problems, highlighting the importance of aligning architecture with physical coupling.

Cite This Study

Kobayashi et al. (2025) studied this question.

synapsesocial.com/papers/68f5fcdc8d54a28a75cf25d7 https://doi.org/10.48550/arxiv.2507.03660

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