sciml.ai | The SciML Scientific Machine Learning Software Organization | Machine Learning library
kandi X-RAY | sciml.ai Summary
kandi X-RAY | sciml.ai Summary
SciML is an open source software organization created to unify the packages for scientific machine learning. This includes the development of modular scientific simulation support software, such as differential equation solvers, along with the methodologies for inverse problems and automated model discovery. By providing a diverse set of tools with a common interface, we provide a modular, easily-extendable, and highly performant ecosystem for handling a wide variety of scientific simulations.
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QUESTION
In the code taken from: https://tutorials.sciml.ai/html/models/01-classical_physics.html as given below:
...ANSWER
Answered 2021-Apr-24 at 15:17I don't understand the usage of .= operator in the function harmonicoscillator. [...] It is not vectorizing ddu because RHS is all scalar.
It is; u, du, and ddu are not scalars, they are length-1 vectors.
You can ask Julia what the .= syntax means:
QUESTION
Let's suppose the example 1 of the bouncing ball with multiple walls in the page:
https://diffeq.sciml.ai/stable/features/callback_functions/
And consider the condition:
...ANSWER
Answered 2021-Feb-08 at 08:30Consider the code later on on the same page you listed:
QUESTION
I have been trying to replicate https://diffeqflux.sciml.ai/dev/examples/BayesianNODE_NUTS/, using different ODE equation, but I have received this result without uncertainty quantification, is it because I did the initial value u0 is higher :
Could you please tell me what was wrong?
...ANSWER
Answered 2021-Feb-01 at 12:14The most likely reason for this is because the loss function magnitude is too high for the posterior samples, due to which the posterior sample results are out of range and not visible on your plot.
This can be possibly fixed by (a) adding a scaling factor the Neural ODE output and making sure that the loss function does not start from a very high magnitude or (b) increasing the number of layers in the neural network architecture/ changing the activation function.
QUESTION
I want to implement the adjoint sensitivity analysis in python, in order to determine the gradient of my objective function with respect to some parameters. In specific the objective function depends on the solution of a differential equation which in turn depends on said parameters which I am looking to find the optimum of.
To perform this there are numerous good packages both in Julia (see here), as well as CVODES from SUNDIALS, however the latter which does apparently have a wrapper made for python, does not include sensitivity analysis capabilities according to this link. Furthermore, I have looked into SALib for sensitivity analysis, but as far as I understand this refers to some other type of 'sensitivity analysis' and therefore adjoint or even forward sensitivity analysis is not included (correct me if I am wrong on this one).
Thus my question is, does a version of CVODES exist in python with sensitivity analysis capabilities, or is there there any other package where one can use in order to perform adjoint sensitivity analys?
...ANSWER
Answered 2020-Dec-15 at 12:08You can easily call Julia code / packages from Python with pyjulia. https://github.com/JuliaPy/pyjulia
QUESTION
I'm new with Julia trying to run the example proposed at https://diffeqflux.sciml.ai/stable/examples/normalizing_flows/ to define and train a continuous normalizing flow using sciml_train.
I just copy/pasted the written code and gets the following error:
...ANSWER
Answered 2020-Oct-09 at 21:08Given, the error message seems a little bit cryptic, due to issuing "#5#7" instead of a proper function name for the method which is seemingly not callable for the two input arguments of types ::Array{Float32,1}, ::Float32.
This is presumably due to some not properly defined variable/symbol, which is assumed by a caller to be callable like a function. The caller probably seems to be stemming from within the code you are using.
The thing which seems like the most probable source of this error seems to me to be the cb in res1 = DiffEqFlux.sciml_train(loss_adjoint, ffjord_test.p, ADAM(0.1), cb = cb, maxiters = 100) . It is meant to be shorthand for "callback" and assumed to be a function defined by the user, and is called from within sciml_train. Comparing with the linked document where you took the example from, I can verify that your snippet matches the code over there. I haven't tested it out myself by now, but could you check that the error is gone as soon as you either define a cb function or remove it from the function call? If that'd be the case, there'd be an error within the official documentation.
QUESTION
I want to solve the matrix-form time-dependent Schrodinger equation on 3d lattice with DifferentialEquations.jl,
i.e., (∂/∂t)ψ = -iHψ ,where ψ is a vector and H is a (time-independent) matrix.
I tried to write the code like this.
ANSWER
Answered 2020-Aug-30 at 15:31ψ0 = [] # Initial conditions
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