Greg Hurst, Wolfram|Alpha Math Content
Matt Gelber伊利诺伊州的博士后,大学厄巴纳 - 尚佩恩分校

In past blog posts, we’ve talked about theWolfram Language’s built-in, high-level functionality for 3D printing. Today we’re excited to share an example of how some more general functionality in the language is being used topush the boundaries of this technology。Specifically, we’ll look at how computation enables 3D printing of very intricate sugar structures, which can be used to artificially create physiological channel networks like blood vessels.

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September 11, 2018 —Jon McLoone, Director, Technical Communication & Strategy

Having a really broad toolset and an open mind on how to approach data can lead to interesting insights that are missed when data is looked at only through the lens of statistics or machine learning. It’s something we at Wolfram Research callmultiparadigm data science, which I use here for a small excursion through calculus, graph theory, signal processing, optimization and statistics to gain some interesting insights into the engineering of supersonic cars.

Car gauges

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September 30, 2016 —John McGee,应用开发者,沃尔夫勒姆科技集团manbet万博app


AMersenne primeis a prime number of the formMp= 2p- 1,其中指数pmust also be prime. These primes take their name from the French mathematician and religious scholarMarin Mersenne,谁生产这种形式在十七世纪上半叶的素数的列表。自古以来人们已经知道的是,这些第4位,M2= 3,M3= 7,
M5= 31 andM7= 127, are prime.

Marin Mersenne

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August 26, 2016 —咱ch Littrell, Technical Content Writer, Technical Communications and Strategy Group

We are constantly surprised by what fascinating applications and topics Wolfram Language experts are writing about, and we’re happy to again share with you some of these amazing authors’ works. With topics ranging from learning to use theWolfram Languageon a Raspberry Pi to a groundbreaking book with a novel approach to calculations, you are bound to find a publication perfect for your interests.

Getting Started with Wolfram Language and Mathematica for Raspberry Pi, Essentials of Programming in Mathematica, Geospatial Algebraic Computations, Theory and Applications

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July 9, 2013 —Paritosh Mokhasi, Kernel Developer, Algorithms R&D


da Vinci sketch

We would now call such swirling motions vortices, and we have a systematic way of understanding the behavior of fluids through the Navier–Stokes equations. Let’s first start with understanding these equations.

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May 30, 2013 —Wolfram Blog Team,钨manbet万博app博客团队

UsingMathematica, Wolfgang Schmidt, a scientist at the Jülich Centre for Neutron Science, designed new neutron optical components to improve the efficiency of one of the most powerful spectrometers available for neutron scattering research.

Mathematica‘s flexible programming language allowed Schmidt to quickly write new programs and verify lengthy calculations for simulations he needed to investigate for spectrometer upgrades, which included a neutron polarizer. WithMathematica,他可以测试和可视化,帮助他设计的偏光器和优化其性能的各种参数。

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Ulises Cervantes-Pimentel, Senior Kernel Developer
Abdul Dakkak, Senior Compiler Developer, Compiler Development

上周我们posted an itemaboutmanbet万博app沃尔夫勒姆研究‘s partnership withNVIDIA以GPU编程融入Mathematica。With NVIDIA’sGPU Technology Conference 2010starting today, we thought we would share a little more for those who won’t be at the show to see us (booth #31, for those who are attending).

Mathematica‘s GPU programming integration is not just about performance. Yes, of course, with GPU power you get some of your answers several times faster than before—but that is only half the story.

The heart of the integration is the full automation of the GPU function developing process. With proper hardware, you can write, compile, test, and run your code in a single transparent step. There is no need to worry about details, such as memory allocation or library binding.Mathematicahandles it elegantly and gracefully for you. As a developer, you will be able to focus on developing and optimizing your algorithms, and nothing else.

Here are a couple of examples to give you a taste of the upcoming feature.

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September 14, 2010 —Angela Sims, Government Account Executive, Government Sales


CUDA is NVIDIA’s performance computing architecture that harnesses modern GPU’s potential. The new partnership means that if you have GPU-equipped hardware, you can transformMathematica‘s computing, modeling, simulation, or visualization performance, boosting speed by factors easily exceeding 100. Now that’s fast!

Afraid of the programming involved? Don’t be.Mathematica‘s new CUDA programming capabilities dramatically reduce the complexity of coding required to take advantage of GPU’s parallel power. So you can focus on innovating your algorithms rather than spending time on repetitive tasks, such as GUI design.

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March 31, 2009 —Joel Klein分布式系统工程经理

For some people,parallel computingand the need for a cluster is a way of life. For others, the need sneaks up on them. Most clusters and grids are planned and organized from the first, and that can take time and effort, to say nothing of configuration. Other times there’s no budget for new hardware, but there are computer labs or desktop computers unused for much of the day—a cluster waiting to be harnessed, if only you can get the Macs to talk to the Windows boxes, and keep straight all the hostnames in use. For situations like these I helped developWolfram Lightweight Grid System, which is designed from the ground up to let you assemble existing hardware into a self-organized network, accessible fromMathematicawith almost no configuration.

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March 18, 2009 —Roman Maeder主任并行计算技术

In the eighties I attended a scientific presentation about a rather cumbersome way to parallelize one of the symbolic computation systems in existence at that time and quickly realized how much more elegantly I could bring parallelism toMathematica, thanks to its symbolic communication protocol,MathLink。该协议允许我之间并发运行的交换,不仅数据,而且还计划Mathematicakernels.

The result was a package, written entirely inMathematica, calledParallel Computing Toolkit。而此时并行计算意味着大的昂贵的机器,FORTRAN,和批处理作业时,它是相当满足从交互与不同的并行范式进行实验Mathematicanotebook, with a couple of machines on a local network doing the computations, and be able to do parallel functional programming and work with symbolic expressions and arbitrary-precision arithmetic in parallel. I got a lot of surprised reactions from people who thought that parallelization is this big complicated thing, requiring supercomputers and large funds, and rather large problems, to be worthwhile. The truth is, most problems people solve are easy to parallelize.

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