universal-approximation-theorem

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jbramburger /DataDrivenDynSyst

Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems

data-science machine-learning neural-network forecasting autoencoder dynamical-systems kernel-methods conservation-laws autoencoders sindy dynamic-mode-decomposition time-delay stroboscopic-map poincare-map autoencoder-neural-network extended-dynamic-mode-decomposition physics-informed-learning sindy-algorithm physics-informed-neural-networks universal-approximation-theorem

UpdatedOct 28, 2025
Jupyter Notebook

Isaac-Somerville /Neural-Networks-for-Solving-Differential-Equations

Star14

Codebase for Master's dissertation in Mathematics at Durham University. Topic: applying neural networks to differential equations. Grade: 85/100.

python automatic-differentiation mathematics pytorch neural-networks differential-equations durham-university curriculum-learning burgers-equation three-body-problem physics-informed-neural-networks universal-approximation-theorem

UpdatedAug 17, 2023
Python

mehulthakral /logic_detector

Star5

A Machine Learning based system to detect semantics in the form of semantic label and suggest optimized alternatives for Python and C++ function-based code snippet.Semantic Detection is language agnostic with accuracy above 95% while optimization with accuracy above 85% for both Python and C++.

machine-learning deep-learning naive-bayes-classifier polynomial-regression code-optimization cyclomatic-complexity halstead-metrics memory-profiling semantic-detection universal-approximation-theorem machine-programming time-profiling

UpdatedMay 5, 2021
JavaScript

dishant2009 /Neural-Network-Universal-Approximation-Theorem-Demonstration

Star2

This interactive web application demonstrates how neural networks can approximate virtually any continuous function, showcasing the Universal Approximation Theorem through real-time visualization.

visualization neural-network animation curve-fitting universal-approximation-theorem

UpdatedJun 12, 2025
HTML

msmrexe /interpretable-neural-basis-decomposition

Star1

A mechanistic interpretability framework visualizing the Universal Approximation Theorem. It deconstructs Neural Networks into weighted ReLU basis functions to reveal how models construct complex non-linear topologies from piecewise linear segments.

machine-learning topology numpy university-project pytorch decomposition course-project optimization-algorithms interpretability adam-optimizer momentum-strategy manifold-visualization universal-approximation-theorem adaptive-moment-estimation mechanistic-interpretability mlp-from-scratch

UpdatedNov 20, 2025
Python

Gehoren /interpretable-neural-basis-decomposition

Star0

🔍 Explore how Multi-Layer Perceptrons work by visualizing function approximation through Neural Basis Decomposition and Mechanistic Interpretability techniques.

UpdatedDec 18, 2025
Python

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