Physics-informed neural networks need a physicist to be accurate: the case of mass and heat transport in Fischer-Tropsch catalyst particles

Physics-Informed Neural Networks (PINNs) have emerged as an influential technology, merging the swift and automated capabilities of machine learning with the precision and dependability of simulations grounded in theoretical physics. PINNs are often employed to solve algebraic or differential equati...

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Hauptverfasser:	Nikolaienko, Tymofii, Patel, Harshil, Panda, Aniruddha, Joshi, Subodh Madhav, Jaso, Stanislav, Kalyanaraman, Kaushic
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Artificial Intelligence Computer Science - Learning Physics - Chemical Physics Physics - Computational Physics
Online-Zugang:	Volltext bestellen
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