Computational Sensing, Understanding, and Reasoning: An Artificial Intelligence Approach to Physics-Informed World Modeling

Moya, Beatriz (Universidad de Zaragoza) ; Badías, Alberto ; González, David (Universidad de Zaragoza) ; Chinesta, Francisco ; Cueto, Elias (Universidad de Zaragoza)

Resumen: This work offers a discussion on how computational mechanics and physics-informed machine learning can be integrated into the process of sensing, understanding, and reasoning of physical phenomena. A foundation in physics can leverage interpretability, data efficiency, and generalization of the models sought for the dynamics of complex physical systems. Consequently, this synergy results in promising approaches to develop world models that are capable of performing accurate and reliable simulations (reasoning) in low-data regimes. Among the possible alternative formulations, we highlight how thermodynamics offers a general framework to construct inductive biases, demonstrating its potential in applications where physics-consistent predictions are essential.

Idioma: Inglés

DOI: 10.1007/s11831-023-10033-y

Año: 2023

Publicado en: ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 31, 4 (2023), 1897-1914
ISSN: 1134-3060

Factor impacto JCR: 9.7 (2023)

Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 6 / 169 = 0.036 (2023) - Q1 - T1
Categ. JCR: MATHEMATICS, INTERDISCIPLINARY APPLICATIONS rank: 2 / 135 = 0.015 (2023) - Q1 - T1
Categ. JCR: ENGINEERING, MULTIDISCIPLINARY rank: 3 / 179 = 0.017 (2023) - Q1 - T1

Factor impacto SCIMAGO: 1.801 - Computer Science Applications (Q1) - Applied Mathematics (Q1)

Financiación: info:eu-repo/grantAgreement/EUR/MICINN/TED2021- 130105B-I00

Tipo y forma: Artículo (PostPrint)

Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)