The simplest way to serve AI/ML models in production

Truss helps you build go-kit microservices without having to worry about writing or maintaining boilerplate code.

Modern Cryptographic Firmware

Micro toolkit for Clojure/Script errors

USWDS 3.0 components built in React

Examples of models deployable with Truss

A package for binary and continuous, single and multi-material, truss and continuum, 2D and 3D topology optimization on unstructured meshes using automatic differentiation in Julia.

Example of a Truss Terraform project

A Python library for structural analysis using the finite element method

All nature-inspired algorithms involve two processes namely exploration and exploitation. For getting optimal performance, there should be a proper balance between these processes. Further, the majority of the optimization algorithms suffer from local minima entrapment problem and slow convergence speed. To alleviate these problems, researchers are now using chaotic maps. The Chaotic Gravitational Search Algorithm (CGSA) is a physics-based heuristic algorithm inspired by Newton's gravity principle and laws of motion. It uses 10 chaotic maps for global search and fast convergence speed. Basically, in GSA gravitational constant (G) is utilized for adaptive learning of the agents. For increasing the learning speed of the agents, chaotic maps are added to gravitational constant. The practical applicability of CGSA has been accessed through by applying it to nine Mechanical and Civil engineering design problems which include Welded Beam Design (WBD), Compression Spring Design (CSD), Pressure Vessel Design (PVD), Speed Reducer Design (SRD), Gear Train Design (GTD), Three Bar Truss (TBT), Stepped Cantilever Beam design (SCBD), Multiple Disc Clutch Brake Design (MDCBD), and Hydrodynamic Thrust Bearing Design (HTBD). The CGSA has been compared with seven state of the art stochastic algorithms particularly Constriction Coefficient based Particle Swarm Optimization and Gravitational Search Algorithm (CPSOGSA), Standard Gravitational Search Algorithm (GSA), Classical Particle Swarm Optimization (PSO), Biogeography Based Optimization (BBO), Continuous Genetic Algorithm (GA), Differential Evolution (DE), and Ant Colony Optimization (ACO). The experimental results indicate that CGSA shows efficient performance as compared to other seven participating algorithms.

Kotlin and Java wrappers around SpannableStringBuilder (Inspired by JakeWharton's Truss and Kotlin's kotlinx.html)

Designing bridge trusses with Pytorch autograd

Nonlinear Truss Solver for Python

A finite element analysis library for 2D frame, beams and truss elements using C#

A wrapper class on top of SpannableStringBuilder with utility methods for android and custom spans.

TrussC main repository

Inverse design of 3D truss networks with automatic differentiation

Golang SDK for Truss

picotrace - system call process tracer

FIDO authenticator Trussed app

Free online structural analysis tool for beams and trusses. Edubeam lets you create and solve 2D structures directly in your browser — no installation required. Instantly generate bending moment diagrams, shear force diagrams, and deflection plots. Ideal for students, educators, and engineers.

Truss Analysis Software Using The Finite Element Method

Truss analysis software using the Finite Element Method written in Python.

Source code for the paper "Volumetric Michell Trusses for Parametric Design and Fabrication" published at SCF 2019.

slientruss3d : Python for stable truss analysis and optimization tool

A TypeScript DSL for writing utility CSS in React/JSX

Implementation of 'Inverting the structure-property map of truss metamaterials via deep learning' (PNAS)

Hardware Verification library for C++, SystemC and SystemVerilog

No description available

go-kit微服务套件的使用，涉及的内容有gin、grpc、服务发现、负载均衡、限流器、熔断器、错误处理、日志、监控、分布式追踪、jwt