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@llfung talked to me today about his work on adjoint-accelerated programmable inference for large PDEs, and what would be needed on Turing's part to support that. As I understand it (and I know very l…
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Hi, could you please help me out
In Matlab when I run steady-state sensitivities in the case of _adjoint_ sensitivities
```
options_ss.sensi_meth = 'adjoint';
```
_sllh_ vector has **all** zeroes…
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**What did you expect to happen?**
Sensitivities to be computed
**What has happened instead?**
`rdata.x` and `rdata.sx` are zero matrices
**To Reproduce**
Compile a model with algebraic expre…
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Motivated by https://github.com/AMICI-dev/AMICI/issues/1230
Should be done for both forward and adjoint sensitivities (#18).
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### Feature description
I think this feature is a fairly simple one. The implementation is not exactly clear to me as the whole package is really centered around the use of current functions.
In …
mleot updated
2 months ago
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There are two approaches available in AMICI for state sensitivities computation at steady state: numerical integration and solving a linear system (`computeNewtonSensis`). There are now also three `St…
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Tentative outline
- [ ] pre-requisites : installing python (anaconda), git, using the jupyter notebook
- [ ] quick overview of python, numpy, scipy
- [ ] overview of SimPEG framework and gradient ba…
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For simple cases we can just differentiate through the DDE solver but if the DDE system contains parameter-dependent C1-discontinuities the forward sensitivities have jump discontinuities which, e.g.,…
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The fisher information matrix (basically the hessian with respect to the cost function) is frequently used in systems biology for practical identifiability analysis. I think this would be useful to ha…
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## For reference: the forward problem
For a single electric source term, the forward problem can be defined as:
```math
\begin{bmatrix}
\mathbf{A_{dc}} & & & & & \\
\mathbf{B_1 G} & \mathbf{A…