Zobrazeno 1 - 10
of 245
pro vyhledávání: '"Cueto Elias"'
The growing importance of real-time simulation in the medical field has exposed the limitations and bottlenecks inherent in the digital representation of complex biological systems. This paper presents a novel methodology aimed at advancing current l
Externí odkaz:
http://arxiv.org/abs/2412.12034
Autor:
Tierz, Alicia, Iparraguirre, Mikel M., Alfaro, Iciar, Gonzalez, David, Chinesta, Francisco, Cueto, Elias
We explore the feasibility of foundation models for the simulation of physical phenomena, with emphasis on continuum (solid and fluid) mechanics. Although so-called learned simulators have shown some success when applied to specific tasks, it remains
Externí odkaz:
http://arxiv.org/abs/2410.14645
Thermodynamics-informed neural networks employ inductive biases for the enforcement of the first and second principles of thermodynamics. To construct these biases, a metriplectic evolution of the system is assumed. This provides excellent results, w
Externí odkaz:
http://arxiv.org/abs/2405.13093
The development of inductive biases has been shown to be a very effective way to increase the accuracy and robustness of neural networks, particularly when they are used to predict physical phenomena. These biases significantly increase the certainty
Externí odkaz:
http://arxiv.org/abs/2404.01060
We present a method to increase the resolution of measurements of a physical system and subsequently predict its time evolution using thermodynamics-aware neural networks. Our method uses adversarial autoencoders, which reduce the dimensionality of t
Externí odkaz:
http://arxiv.org/abs/2402.17506
Autor:
Reille Agathe, Champaney Victor, Daim Fatima, Tourbier Yves, Hascoet Nicolas, Gonzalez David, Cueto Elias, Duval Jean Louis, Chinesta Francisco
Publikováno v:
Mechanics & Industry, Vol 22, p 32 (2021)
Solving mechanical problems in large structures with rich localized behaviors remains a challenging issue despite the enormous advances in numerical procedures and computational performance. In particular, these localized behaviors need for extremely
Externí odkaz:
https://doaj.org/article/76a0cf28c57e4b4ea3509772873ec577
We develop inductive biases for the machine learning of complex physical systems based on the port-Hamiltonian formalism. To satisfy by construction the principles of thermodynamics in the learned physics (conservation of energy, non-negative entropy
Externí odkaz:
http://arxiv.org/abs/2211.01873
The imminent impact of immersive technologies in society urges for active research in real-time and interactive physics simulation for virtual worlds to be realistic. In this context, realistic means to be compliant to the laws of physics. In this pa
Externí odkaz:
http://arxiv.org/abs/2210.13414
Autor:
Cueto, Elias, Chinesta, Francisco
Thermodynamics 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
Externí odkaz:
http://arxiv.org/abs/2207.12749
Learning and reasoning about physical phenomena is still a challenge in robotics development, and computational sciences play a capital role in the search for accurate methods able to provide explanations for past events and rigorous forecasts of fut
Externí odkaz:
http://arxiv.org/abs/2203.05775