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universal-approximation-theorem

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jbramburger / DataDrivenDynSyst

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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
  • Updated Mar 21, 2024
  • Jupyter Notebook

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

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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
  • Updated Aug 17, 2023
  • Python

mehulthakral / logic_detector

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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
  • Updated May 5, 2021
  • JavaScript

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