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Workshop: NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences

Physics-consistency of infinite neural networks

Sascha Ranftl


Recent work demonstrates the integration of physics prior knowledge into neural networks through neural activation functions and the infinite-width correspondence to Gaussian processes, provided the Central Limit Theorem holds. Together with the construction of physics-consistent Gaussian process kernels, former connection begs the question for physics-consistent infinite neural networks. So construed regression models find specialized applications such as inverse problems, uncertainty quantification, and optimization, particularly in data-scarce situations. These 'surrogate' models can efficiently learn from limited data while maintaining physical consistency.

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