Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...

Eric Gutiérrez, 6th February 2026. A Python implementation of a 1-hidden layer neural network built entirely from first principles. This project avoids deep learning libraries (like TensorFlow or ...

Implement Neural Network in Python from Scratch ! In this video, we will implement MultClass Classification with Softmax by making a Neural Network in Python from Scratch. We will not use any build in ...

Abstract: Defects reduce the bearing capacity of underwater structures. The determination of defect size provides guidance for underwater structure safety assessment and maintenance. Alternating ...

Abstract: Infrared Local Positioning Systems (IRLPS) offer a cost-effective and accurate alternative for indoor localization, where GNSS signals are typically not available. Despite their potential, ...

The package contains a mixture of classic decoding methods and modern machine learning methods. For regression, we currently include: Wiener Filter, Wiener Cascade, Kalman Filter, Naive Bayes, Support ...

3D rendering—the process of converting three-dimensional models into two-dimensional images—is a foundational technology in computer graphics, widely used across gaming, film, virtual reality, and ...

What California does is define neural data as sensitive data and give it those protections only when it's being used for the purposes of inferring characteristics about someone. But Connecticut takes ...

Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Grudziądzka 5, 87-100 Toruń, Poland ...