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Thermodynamics of Learning Physical Phenomena
https://orcid.org/0000-0003-1017-4381
AbstractThermodynamics could be seen as an expression of physics at a high epistemic level. As such, its potential as an inductive bias to help machine learning procedures attain accurate and credible predictions has been recently realized in many fields. We review how thermodynamics provides helpful insights in the learning process. At the same time, we study the influence of aspects such as the scale at which a given phenomenon is to be described, the choice of relevant variables for this description or the different techniques available for the learning process.
Thermodynamics of Learning Physical Phenomena
https://orcid.org/0000-0003-1017-4381
AbstractThermodynamics could be seen as an expression of physics at a high epistemic level. As such, its potential as an inductive bias to help machine learning procedures attain accurate and credible predictions has been recently realized in many fields. We review how thermodynamics provides helpful insights in the learning process. At the same time, we study the influence of aspects such as the scale at which a given phenomenon is to be described, the choice of relevant variables for this description or the different techniques available for the learning process.
Thermodynamics of Learning Physical Phenomena
https://orcid.org/0000-0003-1017-4381
AbstractThermodynamics could be seen as an expression of physics at a high epistemic level. As such, its potential as an inductive bias to help machine learning procedures attain accurate and credible predictions has been recently realized in many fields. We review how thermodynamics provides helpful insights in the learning process. At the same time, we study the influence of aspects such as the scale at which a given phenomenon is to be described, the choice of relevant variables for this description or the different techniques available for the learning process.
Thermodynamics of Learning Physical Phenomena
Arch Computat Methods Eng
Cueto, Elias (author) / Chinesta, Francisco (author)
Archives of Computational Methods in Engineering ; 30 ; 4653-4666
2023-11-01
Article (Journal)
Electronic Resource
English
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