Artificial intelligence (AI)-based methods continue to make inroads into accelerated
materials design and development. Here, we review AI-enabled advances made in the …

This research aims to study and assess state-of-the-art physics-informed neural networks
(PINNs) from different researchers' perspectives. The PRISMA framework was used for a …

Neural operators can learn nonlinear map**s between function spaces and offer a new
simulation paradigm for real-time prediction of complex dynamics for realistic diverse …

Neural networks (NNs) are currently changing the computational paradigm on how to
combine data with mathematical laws in physics and engineering in a profound way …

Physics informed neural networks (PINNs) are a novel deep learning paradigm primed for
solving forward and inverse problems of nonlinear partial differential equations (PDEs). By …

Understanding real-world dynamical phenomena remains a challenging task. Across
various scientific disciplines, machine learning has advanced as the go-to technology to …

This article provides an overview of multi-fidelity modeling trends. Fidelity in modeling refers
to the level of detail and accuracy provided by a predictive model or simulation. Generally …

Highly accurate numerical or physical experiments are often very time-consuming or
expensive to obtain. When time or budget restrictions prohibit the generation of additional …

Uncertainty quantification (UQ) in machine learning is currently drawing increasing research
interest, driven by the rapid deployment of deep neural networks across different fields, such …

Data-driven discovery of governing equations in computational science has emerged as a
new paradigm for obtaining accurate physical models and as a possible alternative to …