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September 10, 2025Mathematische SemesterberichteOpen Access

Scientific machine learning

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Authors

FDFelix DietrichTechnical University of Munich WSW.H.A. SchildersNetherlands Institute for Advanced Study in the Humanities and Social Sciences

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Implication

This overview highlights data-driven methods and physics-based models in scientific machine learning, demonstrating their effectiveness in tackling complex challenges.

Key Points

Scientific machine learning integrates data-driven methods and physics-based models for modeling and simulation.
Core challenges include data scarcity, physical consistency, and computational cost, which hinder traditional methods.
Methodologies such as physics-informed neural networks and operator learning address specific limitations in analysis.
Applications in fluid dynamics and energy management demonstrate enhanced accuracy and efficiency in solutions.

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Dietrich et al. (2025) studied this question.

synapsesocial.com/papers/68c192459b7b07f3a06166b1 https://doi.org/10.1007/s00591-025-00399-4

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Implication

This overview highlights data-driven methods and physics-based models in scientific machine learning, demonstrating their effectiveness in tackling complex challenges.

Scientific machine learning

Discussion

Member takes

Implication

Key Points

Cite This Study

Scientific machine learning

Discussion

Member takes

Implication

Key Points

Cite This Study

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