OpenAI's report examines eight agent‑assisted scientific‑computing projects, showing how coding agents can rewrite legacy tools like STAR in Rust with near‑perfect result consistency, accelerate workloads, and highlight the need for human validation, iterative feedback, and sustainable long‑term maintenance.
The author curates a collection of 407 recent large‑model papers—264 frontier works across six innovation paths and 143 top‑conference papers—classifies them into 14 hot sub‑topics, and explains how labs can match these directions to their available compute, data, and time resources.
The article compiles 184 practical PINN innovations—including theory advances, new training paradigms, and integrations with Bayesian methods, reinforcement learning, Transformers, and graph neural networks—along with ready-to-use source code and starter resources for researchers seeking cutting‑edge physics‑informed neural network solutions.
The article examines how AI‑native scientific agents demand flexible, secure, and observable infrastructure, and how Alibaba Cloud’s Serverless‑based AgentRun platform delivers extreme elasticity, cost reduction, stateful long‑running support, sandbox security, and full‑chain tracing to enable rapid deployment of tens of thousands of research tools.
This article reviews the highly cited 2019 JCP paper that introduced Physics‑Informed Neural Networks, explains their core idea of embedding PDE residuals into the loss, compares them with contemporaneous methods, details implementation choices, showcases forward and inverse experiments, and discusses their impact, limitations, and future research directions.
The article explains how the phpy extension lets PHP import Python libraries like numpy, demonstrates array and matrix operations with operator overloading, compares PHP and Python operators, and shows the new concise syntax introduced in phpy 2.2 for more readable scientific code.
Google researchers have built a breakthrough AI system that uses large language models combined with tree‑search to automatically write, rewrite, and optimize scientific computing code, delivering expert‑level solutions across fields such as genomics, epidemiology, earth observation, neuroscience, and numerical mathematics.
Google researchers have built a breakthrough AI system that uses large language models and tree‑search to automatically write, rewrite, and optimize scientific computing code, delivering solutions that surpass human experts across biology, epidemiology, remote sensing, neuroscience, time‑series analysis, and computational mathematics.
This article walks through seven core 3D plotting methods using Python's Matplotlib library—covering line, scatter, surface, wireframe, contour, triangulated surface, and Möbius strip visualizations—complete with setup steps, code examples, and practical tips for multivariate data analysis.
Python’s open‑source ecosystem, extensive libraries, and low learning curve are rapidly displacing MATLAB in academic research, industry, and AI development, as universities adopt Python curricula, companies integrate it for data analysis and modeling, while MATLAB retains niche strengths in Simulink and specialized engineering toolboxes.
This article provides a comprehensive overview of NumPy, covering its high‑performance ndarray object, core functionalities such as broadcasting and vectorized operations, array creation and manipulation methods, mathematical and statistical functions, linear‑algebra utilities, random number generation, and input/output capabilities with practical code examples.
This article provides a thorough overview of NumPy, covering its core ndarray structure, various array creation methods, indexing and slicing techniques, vectorized operations with broadcasting, statistical and linear‑algebra functions, file input/output, and a practical data‑analysis example, all illustrated with executable Python code.
PaddlePaddle 3.0, launched on April 1 2025, introduces five core innovations—including dynamic‑static unified automatic parallelism, a training‑inference integrated PIR, high‑order automatic differentiation for scientific computing, a one‑stage CINN compiler, and heterogeneous multi‑chip adaptation—that dramatically reduce distributed‑training code, boost performance up to four‑fold, and extend the framework to aerospace, automotive, meteorology and life‑science applications while remaining fully compatible with the 2.0 API.
This article presents a comprehensive guide to 45 essential NumPy operations, covering array creation, reshaping, arithmetic, statistical functions, linear algebra, and more, each illustrated with concise explanations and ready-to-run Python code examples to help readers efficiently leverage NumPy for scientific computing.
This article presents a thorough tutorial on NumPy array creation, indexing, reshaping, concatenation, splitting, copying, slicing, statistical analysis, boolean indexing, sorting, unique values, broadcasting, merging, insertion, deletion, transposition, flattening, multi‑dimensional merging, random sampling, dot and outer products, cumulative operations, and differences, providing code examples for each to boost data‑processing efficiency in Python.
The CNCC 2024 conference featured a series of expert talks on AI large‑model research, covering paradigm shifts in scientific discovery, knowledge enhancement and governance, data‑infrastructure analytics, vertical‑domain inference, diffusion‑model advances, multimodal model progress, and medical AI applications, illustrating the breadth and impact of large‑model technologies across multiple domains.
The May 2024 TIOBE index shows Fortran re‑entering the top‑10 programming languages, driven by its long‑standing evolution, high performance in scientific computing, free open‑source nature, and sustained market interest compared with newer languages like Kotlin and Rust.
This content introduces Mayavi, Matplotlib, and Plotly as Python libraries for 3D data visualization, providing code examples for creating surface plots, scatter plots, and interactive charts, with a focus on handling large-scale scientific datasets.
This article compiles a curated list of Python programming fundamentals and popular third‑party libraries for data processing, visualization, scientific computing, optimization, and more, linking to detailed tutorials for each topic, including Numpy, Matplotlib, Pandas, Scipy, PuLP, NetworkX, Geatpy, Numba, and Taichi.
This article provides a comprehensive overview of NumPy and pandas, covering ndarray basics, multi‑dimensional array creation, core array attributes, broadcasting, random number generation, reshaping, as well as pandas Series and DataFrame structures, data import/export, grouping, merging, and advanced data manipulation techniques for scientific and data‑analysis tasks.
This tutorial walks through installing the Mayavi library, explains its core mlab module and API, and provides a series of Python code examples—including mesh, points3d, plot3d, imshow, surf, contour3d, and quiver3d—to demonstrate how to create various 3‑D visualizations.
The article explains high‑performance computing (HPC) concepts, including serial and parallel processing, supercomputer performance measured in FLOPS, real‑world scientific applications such as drug discovery and weather forecasting, and the hardware architectures that enable these massive computational capabilities.
This article introduces Matplotlib as a versatile, open‑source Python plotting library, showcasing its 2D and 3D capabilities, various chart types, color‑customization options, LaTeX support, and integration with tools like Seaborn, while also providing installation tips and concise code examples.
Spyder, a powerful native-Python scientific IDE, offers integrated editing, interactive consoles, variable browsing, documentation viewing, and development tools, plus extensibility via plugins and APIs, and can be installed easily through Anaconda or other package managers, making it a versatile choice for engineers, scientists, and data analysts.
Spyder is a native‑Python scientific IDE offering advanced editing, debugging, variable exploration, and interactive consoles, with extensible plugins and API support, and can be installed via Anaconda or other package managers, making it ideal for scientists, engineers, and data analysts.
This article explains how to use Python's NumPy library to translate common mathematical formulas—such as powers, roots, absolute values, vector and matrix operations—into concise, executable code, covering setup, basic operations, and practical examples for data analysis and machine learning.
This article explains how IPython and Jupyter notebooks support multi‑language execution, integrate Fortran via F2PY, and enable parallel and distributed computing with ipyparallel, illustrating practical magic commands, cluster setup, and performance considerations for scientific Python workflows.
PyArmadillo is a newly released Python library that wraps the C++ Armadillo linear algebra package via pybind11, offering Matlab/Octave‑style syntax, extensive matrix and cube operations, high‑performance back‑ends, and easy installation on major operating systems for scientific computing.
This article presents twenty classic NumPy problems covering array lookup, modification, conversion, sampling, slicing, string operations, rounding, reshaping, linear algebra, and more, each accompanied by concise Python code examples and visual illustrations to help you master advanced data manipulation techniques.
The discussion analyzes why creating a domestic MATLAB‑like scientific computing platform in China faces steep technical, talent, market, and funding obstacles, and proposes ecosystem and policy measures to foster homegrown alternatives.
This article surveys the rapid growth of GPU-accelerated high‑performance computing (HPC) applications driven by NVIDIA's ecosystem, detailing the most common scientific fields, the proportion of GPU‑supported tools, and the emerging role of AI as a primary growth engine.
Numba, a JIT compiler for Python, transforms array‑based functions into machine code, delivering speedups of up to 40‑fold for NumPy loops and over 200‑fold for pure Python loops, making data‑intensive tasks dramatically faster without altering original code.
Alibaba’s open‑source Mars engine extends NumPy‑style tensor operations to distributed environments, leveraging GPU acceleration, sparse matrices, and flexible scheduling to dramatically boost scientific and AI workloads beyond single‑machine limits.
Julia, a free open‑source high‑performance dynamic language, combines the ease of scripting with compiled speed, offering JIT compilation, multiple dispatch, and rich scientific libraries, and with its 1.0 release it now boasts strong benchmarks, robust tooling, and growing industry adoption.
This article introduces the GWPY Python package for analyzing LIGO gravitational‑wave data, explains the physics of gravitational waves and LIGO, shows how to install and use the library with object‑oriented examples, and demonstrates data visualization through complete code snippets.
