Abstract: This paper addresses the challenges of applying Physics-Informed Neural Networks (PINN) to complex boundary problems in fluid dynamics, specifically focusing on unsteady cylinder wakes.

Abstract: In this study, a contrast source inversion (CSI) based physics-informed neural network method is proposed for solving inverse scattering problems. The proposed method constructs two sets of ...

Learn how backpropagation works by building it from scratch in Python! This tutorial explains the math, logic, and coding behind training a neural network, helping you truly understand how deep ...

Introduction: Accurate joint kinematics estimation is essential for understanding human movement and supporting biomechanical applications. Although optical motion capture systems are accurate, their ...

πMRF (Physics-informed implicit neural MRF) is a physics-informed unsupervised framework for accurate quantitative parameter mapping via global spatio-temporal inversion. piMRF/ ├── main.py # Runnable ...

In our increasingly electrified world, supercapacitors have emerged as critical components in transportation and renewable energy systems, prized for their remarkable power density, cycling stability, ...

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...

A neural network is a machine learning model originally inspired by how the human brain works (Courtesy: Shutterstock/Jackie Niam) Precision measurements of theoretical parameters are a core element ...

One of the key steps in developing new materials is property identification, which has long relied on massive amounts of experimental data and expensive equipment, limiting research efficiency. A ...

Department of Chemical and Biochemical Engineering, Western University, London, Ontario N6A 5B9, Canada ...