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Thoughts after questions from AmpLab staff Jey Kottalam:
So far we have been providing analytical expressions for the 4th spatial derivatives for the covariance kernel, it is possible for us to explor…
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related #7891
We could add a helper function that computes first and second derivatives for only one element of a partition of the parameter space.
We have several applications where we can comput…
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Hi,
This is somewhat of an open issue. An eigenvalue decomposition is necessary for line mixing ECS computations to be effective. This is done. We also need partial derivatives w.r.t. user input…
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It could be a good idea to include CoolProp. I hope you agree. CoolProp has many analytical partial derivatives and internal caching. We currently try to update ExternalMedia to work with the latest v…
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# Description of feature
When using a dense VLM mesh, `compute_partials` in some components (e.g., eval_mtx in aerodynamics) becomes a bottleneck for derivative computations. These partials can be …
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two ideas to better exploit the structure of the loglikelihood for possibly higher performance or higher accuracy
Currently we compute derivatives directly from the loglike.
- curse of dimension…
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**Abstract**
By default, CVODES uses an internal difference quotient function for dense and band matrices. Per user guide:
> If a matrix-based linear solver module is used (i.e., a non-NULL `S…
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Hi, the example about the QM9 database is very illustrative for me to perform regression of molecular properties on my own dataset.
¿It would be possible to implement the computation of the deriva…
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Hello Joao and Rogerio,
The value of `SquaredFlux.dJ()` seem inaccurate. Attached is a notebook that compares `SquaredFlux.dJ()` to `scipy.optimize.approx_fprime()` with varying epsilon.
Attach…
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I'm loving the provenance in the new documentation. One thing I think that would be helpful is to know which functions have derivatives and which rely on autodiff. It helps to understand from a perfor…