Data-centric Engineering: integrating simulation, machine learning and statistics. Challenges and opportunities

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

(2021) Mochen Liao et al.   Global Change Biology Bioenergy

Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

(2020) Liu Yang et al.   SIAM JOURNAL ON SCIENTIFIC COMPUTING

The frontier of simulation-based inference

(2020) Kyle Cranmer et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

The Role of Big Data in Industrial (Bio)chemical Process Operations

(2020) Isuru A. Udugama et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Integration of data analytics with cloud services for safer process systems, application examples and implementation challenges

(2020) Pankaj Goel et al.   JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES

Simulation assisted machine learning

(2019) Timo M Deist et al.   BIOINFORMATICS

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

(2019) Edward O’Dwyer et al.   APPLIED ENERGY

Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi-Supervised Learning Strategy

(2019) Wei Ma et al.   ADVANCED MATERIALS

Deep Fluids: A Generative Network for Parameterized Fluid Simulations

(2019) Byungsoo Kim et al.   COMPUTER GRAPHICS FORUM

Predicting CO 2 Plume Migration in Heterogeneous Formations Using Conditional Deep Convolutional Generative Adversarial Network

(2019) Zhi Zhong et al.   WATER RESOURCES RESEARCH

Machine Learning for Fluid Mechanics

(2019) Steven L. Brunton et al.   Annual Review of Fluid Mechanics

Adversarial Examples: Attacks and Defenses for Deep Learning

(2019) Xiaoyong Yuan et al.   IEEE Transactions on Neural Networks and Learning Systems

Applications of machine learning methods for engineering risk assessment – A review

(2019) Jeevith Hegde et al.   SAFETY SCIENCE

Machine learning: Overview of the recent progresses and implications for the process systems engineering field

(2018) Jay H. Lee et al.   COMPUTERS & CHEMICAL ENGINEERING

A unified deep artificial neural network approach to partial differential equations in complex geometries

(2018) Jens Berg et al.   NEUROCOMPUTING

The promise of artificial intelligence in chemical engineering: Is it here, finally?

(2018) Venkat Venkatasubramanian   AICHE JOURNAL

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Smart energy and smart energy systems

(2017) Henrik Lund et al.   ENERGY

A review of operational methods of variational and ensemble-variational data assimilation

(2017) R. N. Bannister   QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY

DevOps and Its Practices

(2016) Liming Zhu et al.   IEEE SOFTWARE

Mastering the game of Go with deep neural networks and tree search

(2016) David Silver et al.   NATURE

Taking the Human Out of the Loop: A Review of Bayesian Optimization

(2016) Bobak Shahriari et al.   PROCEEDINGS OF THE IEEE

Human-level control through deep reinforcement learning

(2015) Volodymyr Mnih et al.   NATURE

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

Probabilistic numerics and uncertainty in computations

(2015) Philipp Hennig et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Surrogate-assisted evolutionary computation: Recent advances and future challenges

(2011) Yaochu Jin   Swarm and Evolutionary Computation

Model-based design of experiments for parameter precision: State of the art

(2007) Gaia Franceschini et al.   CHEMICAL ENGINEERING SCIENCE