The use of theory-based knowledge in machine learning models has a major impact on many engineering and physics problems. The growth of deep learning algorithms is closely related to an increasing demand for data that is not acceptable or available in many use cases. In this context, the incorporation of physical knowledge or a priori constraints has proven beneficial in many tasks. On the other hand, this collection of approaches is context-specific, and it is difficult to generalize them to new problems. In this paper, we experimentally compare some of the most commonly used theory-injection strategies to perform a systematic analysis of their advantages. Selected state-of-the art algorithms were reproduced for different use cases to evaluate their effectiveness with smaller training data and to discuss how the underlined strategies can fit into new application contexts.

Theory-Guided Deep Learning Algorithms: An Experimental Evaluation / Monaco, Simone; Apiletti, Daniele; Malnati, Giovanni. - In: ELECTRONICS. - ISSN 2079-9292. - ELETTRONICO. - 11:18(2022), p. 2850. [10.3390/electronics11182850]

Theory-Guided Deep Learning Algorithms: An Experimental Evaluation

Simone Monaco;Daniele Apiletti;Giovanni Malnati
2022

Abstract

The use of theory-based knowledge in machine learning models has a major impact on many engineering and physics problems. The growth of deep learning algorithms is closely related to an increasing demand for data that is not acceptable or available in many use cases. In this context, the incorporation of physical knowledge or a priori constraints has proven beneficial in many tasks. On the other hand, this collection of approaches is context-specific, and it is difficult to generalize them to new problems. In this paper, we experimentally compare some of the most commonly used theory-injection strategies to perform a systematic analysis of their advantages. Selected state-of-the art algorithms were reproduced for different use cases to evaluate their effectiveness with smaller training data and to discuss how the underlined strategies can fit into new application contexts.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2971169