1 citations · 2 across the 6 of their papers we have counts for
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Enhancing material behavior discovery using embedding-oriented Physically-Guided Neural Networks with Internal Variables
Rubén Muñoz-Sierra, Manuel Doblaré, Jacobo Ayensa-Jiménez
Physically Guided Neural Networks with Internal Variables are SciML tools that use only observable data for training and and have the capacity to unravel internal state relations.…
Predicting and explaining nonlinear material response using deep Physically Guided Neural Networks with Internal Variables
Javier Orera-Echeverria, Jacobo Ayensa-Jiménez, Manuel Doblare
Nonlinear materials are often difficult to model with classical state model theory because they have a complex and sometimes inaccurate physical and mathematical description or we…
On the application of Physically-Guided Neural Networks with Internal Variables to Continuum Problems
Jacobo Ayensa-Jiménez, Mohamed H. Doweidar, Jose A. Sanz-Herrera +1
Predictive Physics has been historically based upon the development of mathematical models that describe the evolution of a system under certain external stimuli and constraints. T…
Identification of state functions by physically-guided neural networks with physically-meaningful internal layers
Jacobo Ayensa-Jiménez, Mohamed H. Doweidar, Jose Antonio Sanz-Herrera +1
Substitution of well-grounded theoretical models by data-driven predictions is not as simple in engineering and sciences as it is in social and economic fields. Scientific problems…