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[Bot] Update latest pyMOR
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badge.json

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{"schemaVersion": 1, "label": "docs", "message": "2023.2.0", "color": "green"}
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{"schemaVersion": 1, "label": "docs", "message": "2023.2.0", "color": "green"}

latest.tag

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2023.1.1
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2023.2.0

latest/.buildinfo

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# Sphinx build info version 1
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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: f34393d660beb5d3f17e72279103c3af
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config: 9cff14370f0355e40d1c69f80af0ee57
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tags: 645f666f9bcd5a90fca523b33c5a78b7

latest/_downloads/049752ef291d62bd7bcde4a95068666e/tutorial_bt.ipynb

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latest/_downloads/0747cc4a268a0cc5652b64e85e6124d0/tutorial_basis_generation.md

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Here, `0.0001` and `1.` are the common lower and upper bounds for the
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individual components of all |parameters| of `fom`. In our case `fom`
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expects a single parameter `'diffusion'` of 9 values:
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expects a single parameter `'diffusion'` of 6 values:
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```{code-cell}
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fom.parameters
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Let's assemble and solve this equation system using pyMOR to determine
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the best-approximation error in `trivial_basis` for some test vector
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`V` which we take as another random solution of our {{ Model }}:
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`U` which we take as another random solution of our {{ Model }}:
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```{code-cell}
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U = fom.solve(parameter_space.sample_randomly())
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differences:
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```{code-cell}
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# for some reason V_proj does not carry over from the previous cell
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# for some reason U_proj does not carry over from the previous cell
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U_proj = trivial_basis.lincomb(lambdas.T)
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fom.visualize((U, U_proj, U - U_proj),
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legend=('U', 'U_proj', 'best-approximation err'),

latest/_downloads/1000b3f568829899be8a4f7a0b7f7e9a/tutorial_optimization.ipynb

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