惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

V
Visual Studio Blog
量子位
大猫的无限游戏
大猫的无限游戏
Hugging Face - Blog
Hugging Face - Blog
S
SegmentFault 最新的问题
Blog — PlanetScale
Blog — PlanetScale
月光博客
月光博客
Google DeepMind News
Google DeepMind News
小众软件
小众软件
WordPress大学
WordPress大学
宝玉的分享
宝玉的分享
MongoDB | Blog
MongoDB | Blog
B
Blog RSS Feed
博客园 - Franky
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
B
Blog
博客园 - 聂微东
The GitHub Blog
The GitHub Blog
Recent Announcements
Recent Announcements
Y
Y Combinator Blog
Microsoft Security Blog
Microsoft Security Blog
雷峰网
雷峰网
Jina AI
Jina AI
酷 壳 – CoolShell
酷 壳 – CoolShell

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
Fortran is still alive in aerospace and HPC. Here is why.
I Want To Learn Programming · 2026-06-16 · via DEV Community

I Want To Learn Programming

Every few years someone declares Fortran dead. Meanwhile, the world's weather forecasts, aircraft simulations, and a large share of supercomputer cycles are still running Fortran. A language designed in 1957 is not just surviving in scientific computing, it is often the right tool. Here is why.

What Fortran is actually good at

Fortran (the name comes from "Formula Translation") was built for one thing: fast numerical computation on arrays. Decades of refinement made it exceptional at exactly that.

  • Array performance. Fortran's array model and aliasing rules let compilers optimize numerical loops aggressively, often beating naive C and matching hand-tuned code. For dense linear algebra and grid solvers, this matters enormously.
  • Mature numerical libraries. BLAS, LAPACK, and decades of validated scientific code are Fortran. These are not legacy baggage; they are the trusted, battle-tested foundation that newer languages call into.
  • Stability. Code written decades ago still compiles and runs. For a climate model or a structural solver that has been validated against reality over many years, rewriting it in a trendier language is not an upgrade; it is a risk.

Where it still runs

  • Computational fluid dynamics: the solvers that simulate airflow over a wing.
  • Finite element analysis: structural and stress simulation.
  • Weather and climate models: among the largest scientific codes in existence.
  • High-performance computing: a substantial fraction of supercomputer workloads.

These are not museums. They are active, performance-critical systems where Fortran's strengths line up exactly with the need.

Why it is worth learning

Learning Fortran is not nostalgia; it is access. If you want to work in aerospace, HPC, climate science, or computational physics, you will meet Fortran, and being able to read and extend these codebases is a real, scarce skill. It also teaches you to think about performance and memory layout in a way that makes you a better programmer in any language.

And modern Fortran is not the punch-card language of legend. Recent standards have modules, dynamic memory, and clean array syntax. It is a focused, capable tool for numerical work.

Learn it on real solvers

The aerospace Fortran track teaches modern Fortran by building the numerical core of real solvers, foundations, arrays, procedures, numerical methods, ODEs, linear algebra, CFD, and FEM, compiled with gfortran on the server, the same toolchain used on real clusters, and graded in your browser. The first project is free.

Fortran is not dead. It is doing the heavy lifting, quietly, where performance is not optional.