A review and perspective on hybrid modeling methodologies

The term hybrid modeling refers to the combination of parametric models (typically derived from knowledge about the system) and nonparametric models (typically deduced from data). Despite more than 20 years of research, over 150 scientific publications (Agharafeie et al., 2023), and some recent industrial applications on this topic, the...

An in-depth comparison of linear and non-linear joint embedding methods for bulk and single-cell multi-omics

Multi-omic analyses are necessary to understand the complex biological processes taking place at the tissue and cell level, but also to make reliable predictions about, for example, disease outcome. Several linear methods exist that create a joint embedding using paired information per sample, but recently there has been a rise in the...

Prediction of oil and gas pipeline failures through machine learning approaches: A systematic review

Pipelines are vital for transporting oil and gas, but leaks can have serious consequences such as fires, injuries, pollution, and property damage. Therefore, preserving pipeline integrity is crucial for a safe and sustainable energy supply. The rapid progress of machine learning (ML) technologies provides an advantageous opportunity to...

Deep learning for detecting macroplastic litter in water bodies: A review

Plastic pollution in water bodies is an unresolved environmental issue that damages all aquatic environments, and causes economic and health problems. Accurate detection of macroplastic litter (plastic items >5 mm) in water is essential to estimate the quantities, compositions and sources, identify emerging trends, and design preventive...

Model-based optimization approaches for pressure-driven membrane systems

Membrane technology is commonly used within food, bio- and pharmaceutical processes. Beside single-stage membranes, multi-stage membrane systems are become more popular to improve separation performance. In this review, we present a unified four-phase model-based optimization framework to optimize these systems, using mechanistic models,...

Machine learning in microseismic monitoring

The confluence of our ability to handle big data, significant increases in instrumentation density and quality, and rapid advances in machine learning (ML) algorithms have placed Earth Sciences at the threshold of dramatic progress. ML techniques have been attracting increased attention within the seismic community, and, in particular, in...

Machine Learning-Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions

Surrogate models replace computationally expensive simulations of physically-based models to obtain accurate results at a fraction of the time. These surrogate models, also known as metamodels, have been employed for analysis, control, and optimization of water distribution and urban drainage systems. With the advent of machine learning (ML),...