Integral transforms play a foundational role in applied mathematics, statistics, and theoretical physics, serving as powerful ...

An ANN model offers the most accurate and reliable prediction of bubble-point pressure for Rmelan crude oils. For practical ...

For decades, networks have been built on the same fundamental principle: dedicated hardware appliances for each network function. Need a firewall? Buy a box. Need load balancing? Purchase another ...

This repository contains the code for the paper: “Unraveling biochemical spatial patterns: machine learning approaches to the inverse problem of stationary Turing patterns.” The framework provides ...

Abstract: The Fourier Neural Operator (FNO), a recently proposed neural network designed for solving partial differential equations (PDEs), is being explored to accelerate electromagnetic (EM) ...

Abstract: In this work, we propose a complex-valued neural operator (CV-NeuralOp) based on graph neural networks (GNN) to solve 2D wave equation. Inspired by the Green’s function method for solving ...

Discover a smarter way to grow with Learn with Jay, your trusted source for mastering valuable skills and unlocking your full potential. Whether you're aiming to advance your career, build better ...

We introduce a Physics-Informed Neural Networks(PINN) to solve a relativistic Burgers equation in the exterior domain of a Schwarzschild black hole. Our main contribution is a PINN architecture that ...

physics_informed_neural_network/ ├── app/ # FastAPI application │ ├── __init__.py │ ├── api/ # API endpoints │ │ ├── __init__.py ...