Instant neural graphics primitives: lightning fast NeRF and more

Library for multivariate function approximation with splines (B-spline, P-spline, and more) with interfaces to C++, C, Python and MATLAB

Fast radial basis function interpolation for large scale data

A collection of B-spline tools in Julia

CSE 571 Artificial Intelligence

Reinforcement learning algorithms

Julia Wrapper to the Tasmanian library

Adaptively sampled distance fields in Julia

TorchQuantum is a backtesting framework that integrates the structure of PyTorch and WorldQuant's Operator for efficient quantitative financial analysis.

Basis Function Expansions for Julia

Julia library for function approximation with compact basis functions

An adaptive fast function approximator based on tree search

The tools for proper interactions between ApproxFun.jl and DifferentialEquations.jl for pseudospectiral partial differential equation discretizations in scientific machine learning (SciML)

Multivariate Normal Hermite-Birkhoff Interpolating Splines in Julia

Easy21 assignment from David Silver's RL Course at UCL

Python framework to approximate mathemtical functions

Simple linear regressor that tries to approximate a simple function deployed in Tensorflow 2.0 without Keras

Suite of 1D, 2D, 3D demo apps of varying complexity with built-in support for sample mesh and exact Jacobians

The focus of function approximation problems has been on identifying some suitable function without attempting to gain insight into the mechanism of the system. The performance of the model boils down to interpolation. But, in a more realistic setting, we expect test data from outside the distribution of the training set. To better extrapolate t…