#physics-informed-learning

Harnessing Physics-informed Neural Networks for Simulating Refinery Units

Noura RIAHI, Manuel RAMOS CASTILLO, Baptiste RONDELEUX, Yichang WANG

Structure Beats Data at the Boundary

How to combine machine learning with structured knowledge so your model extrapolates instead of flatlines

Solving the Heat Equation with Physics-Informed Neural Networks: PyTorch vs TensorFlow

Reference: This article builds on my previous work: Solving the Heat Equation with Physics-Informed Neural Networks: A Practical Guide…

Paper Insights: Sobolev Training

I was recently reading a Medium article on Physics-Informed Machine Learning (PIML), and Sobolev Training was mentioned as a research…

Physics-Informed AI: Why LLMs Need Solvers, Constraints, and Physical Laws

Part 1 of 2 — An engineering view of how LLMs, physics-informed ML, and numerical solvers can work together without pretending that AI…

Paper Insights: NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning

Personal Motivation:

Unified Agentic-Quantum–Physics–Causal AI Framework: Next-Generation AI Paradigm for Healthcare &…

Executive Summary

Can AI Discover the Laws of Physics? SINDy vs PINNs Explained for Data Scientists

Why PINNs Fail?

When we express physical systems through partial differential equations, we gain a remarkably powerful advantage: the equation itself…

PINN’ler Neden Başarısız Olur?

Uyarı: Aşağıdaki yazıda teknik terimleri İngilizce olarak kullandım. Bunun imla kurallarına uygun olmadığını biliyorum ancak yazıda…

Solving the Heat Equation with Physics-Informed Neural Networks: A Practical Guide

Introduction

Beyond Pattern Matching : Embedding Physical Law into a Neural Network

A hands-on tutorial on building a Physics-Informed Neural Network (PINN) in PyTorch

Building a Physics-Informed Neural Network (PINN) Using PyTorch From Scratch

Solving the 1D Heat Equation

Understanding Physics-Informed Neural Networks (PINNs)

Physics-Informed Neural Networks (PINNs) are a class of machine learning models that combine data-driven techniques with physical laws…