Can I use it for automatic differentiation?

Absolutely. It supports both forward and reverse mode automatic differentiation, which is essential for modern computational physics and optimization.

What is the difference between SICMUtils and Emmy?

Emmy is the modern name for the library, but they refer to the same Clojure implementation of the original MIT Scheme scmutils library.

What is the primary use of the SICMUtils skill?

SICMUtils is used for symbolic mathematics and simulating classical mechanics based on the 'Structure and Interpretation of Classical Mechanics' (SICM) textbook.

Does this skill support Scheme interoperability?

Yes, it provides SRFI-compatible bridges (bicomodules) that allow Clojure-based SICMUtils modules to communicate with Scheme libraries.

SICMUtils (Emmy) Computational Physics

Name: SICMUtils (Emmy) Computational Physics
Author: plurigrid

byplurigrid

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데이터 과학 및 ML

Provides a comprehensive Clojure library for symbolic mathematics, automatic differentiation, and classical mechanics simulation based on SICM concepts.

SICMUtils (now known as Emmy) is a powerful Clojure implementation of the scmutils library, designed to bring the concepts from 'Structure and Interpretation of Classical Mechanics' into an executable computational environment. This skill enables Claude to perform high-level symbolic algebra, execute forward and reverse mode automatic differentiation, and model complex physical systems using Lagrangian and Hamiltonian mechanics. It is particularly unique for its SRFI-compatible abstractions, which create a compositional bridge between Clojure and Scheme, allowing for sophisticated mathematical interoperability and phase space evolution.

주요 기능

01Forward and reverse mode automatic differentiation for calculus operations

028 GitHub stars

03Differential geometry and phase space evolution via transducers

04Symbolic algebra and simplification for complex mathematical expressions

05SRFI-compatible bridging for Clojure-to-Scheme interoperability

06Advanced Lagrangian and Hamiltonian mechanics modeling primitives

사용 사례

01Deriving and simulating equations of motion for complex dynamical systems

02Bridging legacy Scheme SRFI implementations with modern Clojure physics models

03Performing symbolic differentiation and simplification within Clojure workflows

주요 기능

01Forward and reverse mode automatic differentiation for calculus operations

028 GitHub stars

03Differential geometry and phase space evolution via transducers

04Symbolic algebra and simplification for complex mathematical expressions

05SRFI-compatible bridging for Clojure-to-Scheme interoperability

06Advanced Lagrangian and Hamiltonian mechanics modeling primitives

사용 사례

01Deriving and simulating equations of motion for complex dynamical systems

02Bridging legacy Scheme SRFI implementations with modern Clojure physics models

03Performing symbolic differentiation and simplification within Clojure workflows