On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling.
Jan Niklas FuhgNikolaos BouklasPublished in: CoRR (2021)
Keyphrases
- data driven
- machine learning
- probabilistic model
- machine learning algorithms
- machine learning methods
- computer science
- model driven
- machine learning approaches
- monte carlo
- data mining
- model selection
- parameter estimation
- learning algorithm
- learning models
- learning tasks
- statistical models
- machine learning models
- experimental data
- sample size
- generative model
- text classification
- low dimensional
- pattern recognition
- decision trees
- feature selection