DiffEqSensitivity.jl
SciMLBook
| DiffEqSensitivity.jl | SciMLBook | |
|---|---|---|
| 2 | 4 | |
| 184 | 1,789 | |
| - | 0.8% | |
| 9.5 | 4.9 | |
| almost 2 years ago | 18 days ago | |
| Julia | HTML | |
| GNU General Public License v3.0 or later | - |
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DiffEqSensitivity.jl
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[R] New directions in Neural Differential Equations
One reason is that it's not robust and has some odd counter example cases that can come up where the ODE solver is able to converge rapidly on the original problem but not so rapidly in the integral sense on the derivative values. One such case showed up in this issue, which was the impetus for the change in the forward-mode sense, while the reverse sense was changed in testing with direct quadratures (which will be mentioned in a bit).
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Odd Behavior: Neural network hybrid differential equation example
Thanks for letting us know. The fix is in https://github.com/SciML/DiffEqSensitivity.jl/pull/386 and hopefully that'll get released today.
SciMLBook
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SciML Textbook
I've been working on and off using SciML. I just found out they have an e-book: https://book.sciml.ai/
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What's Great about Julia?
I'm hoping the new SciML docs can become a good enough source for beginners looking to do scientific computing (https://docs.sciml.ai/Overview/stable/). It's not there yet, we literally started redirecting links to the new docs on Monday so that's how new it is, it's already moving in the direction of having a lot of materials for new users (in scientific computing specifically, this is not and will not be a general Julia resource) before ever hitting deeper features.
Though if someone wants to dive deep into the language, I'd plug my own SciML course notes: https://book.sciml.ai/, which again is not for general usage but scientific computing but does show a lot about good programming styles (see https://book.sciml.ai/notes/02-Optimizing_Serial_Code/).
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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.
What are some alternatives?
SciMLSensitivity.jl - A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.
cs229-2019-summer - All notes and materials for the CS229: Machine Learning course by Stanford University
SciMLTutorials.jl - Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
18337 - 18.337 - Parallel Computing and Scientific Machine Learning
diffeqpy - Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
Accessors.jl - Update immutable data
DifferentialEquations.jl - Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
18S096SciML - 18.S096 - Applications of Scientific Machine Learning
StochasticDiffEq.jl - Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
Setfield.jl - Update deeply nested immutable structs.
DiffEqFlux.jl - Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods