Combining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven models.
Timur BikmukhametovJohannes JäschkePublished in: Comput. Chem. Eng. (2020)
Keyphrases
- learning problems
- machine learning algorithms
- machine learning
- data driven
- learning models
- learning tasks
- learning algorithm
- supervised learning
- machine learning methods
- computer science
- artificial intelligence
- process model
- high accuracy
- computational cost
- engineering design
- prior knowledge
- pattern recognition
- machine learning approaches
- probabilistic model
- highly accurate
- error rate
- genetic algorithm
- physical processes
- chemical process
- computational intelligence
- model driven
- development process
- accurate models
- models built
- statistical methods
- design process
- data sets
- natural language processing
- information extraction
- support vector
- decision trees
- feature selection
- computer vision
- information retrieval
- data mining