When engineers at Sumitomo Riko needed to speed up the design cycle for automotive rubber and polymer components, they turned to AI models trained not just on data but on the fundamental equations of ...

A simple random sample is a subset of a statistical population where each member of the population is equally likely to be ...

Researchers at Skoltech have proposed a new approach to training neural networks for wave propagation in absorbing media. The ...

Privacy-preserving feature selection allows identifying more important features while ensuring data privacy, thus enhancing ...

As climate risks intensify and the need for timely, actionable intelligence grows, the challenge facing weather and climate ...

Sunday Imoni, professor of Numerical Analysis, has urged the federal government to recognise mathematics as a strategic ...

I wanted to add a new tutorial to the documentation. My idea is to compare a physics informed neural network (PINN) to traditional numerical methods (like finite element or finite difference) for ...

This study introduces a relatively new numerical technique for solving one-dimensional Fisher’s equation. The proposed numerical technique is a simple direct meshless method, which is based on the ...

ABSTRACT: This work focuses on the development and analysis of a financial system using advanced mathematical modeling techniques. Starting from an ordinary financial system, we extend it to a ...

The elemental imaging of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) provides spatial information on elements and therefore can further investigate the growth or evolution ...

Abstract: In this contribution the authors propose a hybrid Boundary Element Method – Physics Informed Neural Networks (BEM – PINN) approach, to be used for the resolution of partial differential ...