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Hi,
i tried to compile SU2 v7.0.1 on Windows 10.
For this i followed your despription in Docs/Build from Source/Build SU2 on Windows.
I installed MS MPI and MS MPISDK 10.1.2, MinGW 8.1.0, pkg-c…
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## Description
Running the mix test for `mdivide_left` takes almost double the time than running the same for `mdivide_right` (the two test are effectively equivalent). What's funny is that `mdivide_…
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The pull request #2062 implements the jvp of the matrix exponential by using the Fréchet derivative. I think the vjp can be easily added by transposing the input matrix in `expm_frechet`:
```python
…
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The way we define and differentiate `lax.cond` can lead to redundant memory allocation during reverse-mode AD. In particular:
1. Each branch of a conditional takes inputs of its own. Passing the sa…
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**Concisely describe the proposed feature**
I'd like to add the support of adstack to the C backend.
Some of the advanced usage of autodiff seems depends on this feature very much, e.g., `mass_sprin…
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Based on our schedule the next release is set to happen on the 19th of October, which means that the feature freeze will begin on the 12th of October (next Monday).
Release notes will be generated …
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Hey,
I am trying out your lib and it seems nice. I think it would be nice to have some convenience operators like += etc. Also I was wondering why it is not possible to use both normal floating point…
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## Issue description
A function `f(x): R^n -> R^m` will have Jacobian w.r.t x as `[df1(x)/dx, df2(x)/dx, ... df_m(x)/dx]` where each `df_m(x)/dx` is an `R^n` vector.
As far as I know, Pytorch a…
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```
## Classification of MNIST dataset
## with the convolutional neural network known as LeNet5.
## This script also combines various
## packages from the Julia ecosystem with Flux.
using Flux
…
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So I was trying reverse-mode autodiff with a second order ODE problem. Here's what I have so far:
```julia
using DifferentialEquations
using Flux
using DiffEqFlux
u0 = Float32[0.; 2.]
du0 = …