Integrating scientific knowledge with machine learning for engineering and environmental systems
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
Recent trends on hybrid modeling for Industry 4.0
The chemical processing industry has relied on modeling techniques for process monitoring,
control, diagnosis, optimization, and design, especially since the third industrial revolution …
control, diagnosis, optimization, and design, especially since the third industrial revolution …
[PDF][PDF] Integrating physics-based modeling with machine learning: A survey
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
The promise of artificial intelligence in chemical engineering: Is it here, finally?
V Venkatasubramanian - AIChE Journal, 2019 - search.ebscohost.com
The article discusses the presence and potential of Artificial Intelligence in Chemical
Engineering and discusses its background. Topics include the Phases of Artificial …
Engineering and discusses its background. Topics include the Phases of Artificial …
Physics-guided neural networks (pgnn): An application in lake temperature modeling
A Daw, A Karpatne, WD Watkins… - Knowledge Guided …, 2022 - taylorfrancis.com
This chapter introduces a framework for combining scientific knowledge of physics-based
models with neural networks to advance scientific discovery. It explains termed physics …
models with neural networks to advance scientific discovery. It explains termed physics …
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …
essential layer of safety assurance that could lead to more principled decision making by …
Augmenting physical models with deep networks for complex dynamics forecasting
Forecasting complex dynamical phenomena in settings where only partial knowledge of
their dynamics is available is a prevalent problem across various scientific fields. While …
their dynamics is available is a prevalent problem across various scientific fields. While …
Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review
Mathematical modeling and simulation methods are important tools in studying various
processes in science and engineering. In the current review, we focus on the applications of …
processes in science and engineering. In the current review, we focus on the applications of …
Deep neural network-based hybrid modeling and experimental validation for an industry-scale fermentation process: Identification of time-varying dependencies …
Kinetic modeling of fermentation processes is difficult due to the use of micro-organisms that
follow complex reaction mechanisms. Kinetic models are usually not perfect owing to …
follow complex reaction mechanisms. Kinetic models are usually not perfect owing to …
Perspectives on the integration between first-principles and data-driven modeling
Efficiently embedding and/or integrating mechanistic information with data-driven models is
essential if it is desired to simultaneously take advantage of both engineering principles and …
essential if it is desired to simultaneously take advantage of both engineering principles and …