Neural Operators Explained in 3 Minutes! | Fourier Neural Operator (FNO) Intuition & PDE Learning скачать в хорошем качестве

Neural Operators Explained in 3 Minutes! | Fourier Neural Operator (FNO) Intuition & PDE Learning

What if neural networks didn’t just learn functions… but learned operators? In this video, we explore Neural Operators and the Fourier Neural Operator (FNO) , a breakthrough idea that allows machine learning models to learn mappings between functions, not just vectors. Instead of solving partial differential equations (PDEs) repeatedly using expensive numerical solvers, neural operators learn the entire solution process itself. This enables fast prediction across different resolutions, parameters, and physical conditions. You’ll learn: ✅ Why traditional neural networks are limited to finite-dimensional mappings ✅ What it means to learn a mapping between functional spaces ✅ How neural operators approximate PDE solution operators ✅ Why Fourier transforms make FNO efficient and powerful ✅ How global spatial interactions are learned in frequency space 🧠 Topics Covered: ➡️Neural Operators ➡️Fourier Neural Operator (FNO) ➡️Operator Learning ➡️Partial Differential Equations ➡️Scientific Machine Learning ➡️Spectral Deep Learning #machinelearning #NeuralOperators #FourierNeuralOperator #ScientificML #deeplearning #AIForPhysics #PDE #computationalphysics #SpectralMethods #OperatorLearning #mlresearch #AIExplained #EngineeringAI #learnai #3MinuteML