Easy way to differentiate equations.
Multivariate dual number algebra, automatic differentiation
A Javascript library for performing automatic differentiation
Javascript neural networks on top of general scalar/tensor reverse-mode automatic differentiation.
Automatic Differentiation for Javascript
Contracts for aligning the Visual Differentiable Programming framework with the Automatic Differentiation Backend.
WebAssembly bindings for Amari mathematical computing library - geometric algebra, tropical algebra, automatic differentiation, measure theory, fusion systems, and information geometry
Forward + reverse-mode automatic differentiation for MathTS rank-N Tensor
Scalar-based reverse-mode automatic differentiation in TypeScript.
Auto-differentiation library for javascript
Agents should invoke this skill when comparing competing products, services, libraries, tools, vendors, or approaches for market/product positioning, feature matrices, strategic trade-offs, pricing, adoption, or differentiation.
Symbolic differentiation plugin for Math.js
A replacement for setInterval() and setTimeout() which works in unfocused windows.
Product positioning framework for competitive differentiation
automatic differentiation for javascript
CSL style for Differentiation
cellular differentiation for seaport clusters
CSL style for Cell Death & Differentiation
## Overview `autodiff-ts` is a TypeScript implementation of automatic differentiation. Automatic Differentiation (AD) [^1] is a technique for computationally determining the gradient of a function with respect to its inputs. It strikes a balance between t
Symbolic differentiation for structured types with a simple DSL
Advanced mathematical computing library with geometric algebra, tropical algebra, and automatic differentiation for JavaScript/TypeScript
A TypeScript library for symbolic mathematics inspired by SymPy, providing symbolic computation, automatic differentiation, and beautiful mathematical rendering.
Visual terminal wrapper for Claude CLI - adds color-coded title bars, bordered sections, and project differentiation
Node module and CLI tool for perceptual differentiation
An incremental data-parallel dataflow platform
An incremental data-parallel dataflow platform
Proc macros for the arael optimization framework: #[arael::model], constraint codegen
Generic automatic differentiation for Rust
Nonlinear optimization framework with compile-time symbolic differentiation
Automatic differentiation using Bevy ECS as computation graph
surge synthesizer -- rungekutta filter
A Rust library for solving differential equations.
Differential graded algebras for agents — the algebraic structure underlying cohomology
Advanced join patterns in differential dataflow
Simple and powerful global optimization using a self-adapting differential evolution.
A high-performance automatic differentiation library for Rust
differentiation.gem implement a kind of Automatic differentiation algorithm. It can convert Method/Proc to differentiable version.
Differentiate development environment from production
Automate baremetal server actions with iPXE
Differential is a numeric-based library will compare two datasets and give you three levels of comparison: report, group, and item level. Each level provides the sum of each dataset and the difference.
Calculate the difference in time relative to now. Returns readable metrics (e.g. years_ago, days_ago, etc.)
Sabetsuka (差別化) makes it easy and painless to differentiate polynomials
A toolbox of numerical differential equation solvers written in pure Ruby. Currently there are multiple methods available to solve initial value ODEs (Dormand-Prince, Forward Euler, 2nd order Adams-Bashforth), boundary value ODEs (Linear Finite Element Galerkin), 2D Poisson's equation (5-point Laplacian), and the 1D advection equation (Upwind, Lax-Friedrichs, Leapfrog, Lax-Wendroff).
Implement Elastic Sensitivity mechanism which was invented by UBER
TealToad gives you all the tools required to create gradient based AI algorithms such as linear regression and neural networks.
numerical solution for ordinaray differential equations
This gem allows to do simulation runs of systems of ordinary differential equations of one independent variable using numerical Runge-Kutta methods for approximation. Contains some features like calculation of separate additive terms of the differential equations, calculation of custom expressions and logging and printing runs to csv files, which engineers may find convenient.
numerical solution for simultaneous ordinaray differential equations
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