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18337 Alternatives
Similar projects and alternatives to 18337
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InfluxDB
InfluxDB high-performance time series database. Collect, organize, and act on massive volumes of high-resolution data to power real-time intelligent systems.
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NeuralPDE.jl
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
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SciMLBook
Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
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CodeRabbit
CodeRabbit: AI Code Reviews for Developers. Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.
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SciMLBenchmarks.jl
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
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SciMLTutorials.jl
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
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DataDrivenDiffEq.jl
Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
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onyx
Discontinued Distributed, masterless, high performance, fault tolerant data processing (by onyx-platform)
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SuiteSparse.jl
Discontinued Development of SuiteSparse.jl, which ships as part of the Julia standard library.
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SaaSHub
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The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better 18337 alternative or higher similarity.
18337 discussion
18337 reviews and mentions
Posts with mentions or reviews of 18337.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-01-31.
- Hello I wanted to know what would be the best way to get started in Julia and artificial intelligence. I looked around alot of different languages and saw Julia was good for data science and for artificial intelligence but would like to know what would be good ways to just do it. Thank you
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SciML/SciMLBook: Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
This was previously the https://github.com/mitmath/18337 course website, but now in a new iteration of the course it is being reset. To avoid issues like this in the future, we have moved the "book" out to its own repository, https://github.com/SciML/SciMLBook, where it can continue to grow and be hosted separately from the structure of a course. This means it can be something other courses can depend on as well. I am looking for web developers who can help build a nicer webpage for this book, and also for the SciMLBenchmarks.
- Why Fortran is easy to learn
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What are some interesting papers to read?
And why not take a course while you're at it.
- Composability in Julia: Implementing Deep Equilibrium Models via Neural Odes
- [2109.12449] AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia
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Is that true?
Here's a good one. It's in Julia but it should do the trick. The main instructor is the most prolific Julia dev in the world.
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[D] Has anyone worked with Physics Informed Neural Networks (PINNs)?
NeuralPDE.jl fully automates the approach (and extensions of it, which are required to make it solve practical problems) from symbolic descriptions of PDEs, so that might be a good starting point to both learn the practical applications and get something running in a few minutes. As part of MIT 18.337 Parallel Computing and Scientific Machine Learning I gave an early lecture on physics-informed neural networks (with a two part video) describing the approach, how it works and what its challenges are. You might find those resources enlightening.
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[P] Machine Learning in Physics?
It's a very thriving field. If you are interested in methods research and want to learn some of the techniques behind it, I would recommend taking a dive into my lecture notes as I taught a graduate course at MIT, 18.337 Parallel Computing and Scientific Machine Learning, specifically designed to get new students onboarded into this research program.
- MIT 18.337J: Parallel Computing and Scientific Machine Learning
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Stats
Basic 18337 repo stats
14
237
5.7
about 2 years ago
The primary programming language of 18337 is Jupyter Notebook.
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