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Inverted Jacobian products are useful in a variety of algorithms such as the efficient implementation of [Newton's method with regularization](https://math.stackexchange.com/questions/3287587/extracti…
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I could possibly do a session on this, but so could Jonny Law
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## 一言でいうと
ピクセル(RGB)レベルの密なMap構築を行うDense SLAMについて、SLAMの構築プロセスをすべて微分可能にし勾配法による最適化を可能にした研究。フレーム間のマッチング・Map推定・グローバル最適化をそれぞれ微分可能な計算に置き換えている。
![image](https://user-images.githubusercontent.com/544269/9…
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This is more of a question rather than an issue, but is there any plan or opinion on supporting Array API compatibility in autograd? Specifically, I'm wondering about the possibility of implementing m…
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Hi @chakravala,
In your JuliaCon paper, I noticed the statement "Mixed-symmetry algebra with `Leibniz.jl` and `Grassmann.jl`, having the geometric algebraic product chain rule, yields automatic dif…
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DAE index reduction requires that certain equations are differentiated versus time. The symbolic module of Modia contains partial differentiation of certain standard functions. A more general approach…
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Copying here from Slack so that it doesn't disappear.
I added a notebook to https://github.com/RePsychLing/ZiFPsychLing/ showing the forward mode and sketching the reverse-mode automatic differenti…
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Hello,
Thank you so much for your nice package.
There is an issue in convergence. I am trying to solve the PDE [NeuralPDE.jl](https://neuralpde.sciml.ai/stable/examples/wave/). The equations, BCs, t…
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**Describe the bug**
Would it be possible to support float64 types. For some numerical simulations, having float64 is important for the accuracy of the simulation. The goal is to use mlx for automati…
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I'm trying to find an example of how to use automatic differentiation in more complex ODE rather than those vanilla examples in the tutorials. Simple ODEs are quite easy to figure out, but I always ha…