Quo vadis multiscale modeling in reaction engineering?–A perspective
This work reports the results of a perspective workshop held in summer 2021 discussing the
current status and future needs for multiscale modeling in reaction engineering. This …
current status and future needs for multiscale modeling in reaction engineering. This …
Automated evolutionary approach for the design of composite machine learning pipelines
The effectiveness of the machine learning methods for real-world tasks depends on the
proper structure of the modeling pipeline. The proposed approach is aimed to automate the …
proper structure of the modeling pipeline. The proposed approach is aimed to automate the …
Development and Integration of Metocean Data Interoperability for Intelligent Operations and Automation Using Machine Learning: A Review
The current oil industry is moving towards digitalization, which is a good opportunity that will
bring value to all its stakeholders. The digitalization of oil and gas discovery, which are …
bring value to all its stakeholders. The digitalization of oil and gas discovery, which are …
Multi-objective evolutionary design of composite data-driven models
IS Polonskaia, NO Nikitin, I Revin… - 2021 IEEE Congress …, 2021 - ieeexplore.ieee.org
In this paper, a multi-objective approach for the design of composite data-driven
mathematical models is proposed. It allows automating the identification of graph-based …
mathematical models is proposed. It allows automating the identification of graph-based …
Discovery of the data-driven models of continuous metocean process in form of nonlinear ordinary differential equations
Data-driven surrogate models are widely used when the system dynamics equations and
governing models are not known a priori. The form of the differential equation with the …
governing models are not known a priori. The form of the differential equation with the …