Data · dataset · 2022
The Cost-Accuracy Trade-Off In Operator Learning With Neural Networks
Listed in CaltechDATA
The repo contains data for the paper @article{de2022cost, title={The Cost-Accuracy Trade-Off In Operator Learning With Neural Networks}, author={De Hoop, Maarten and Huang, Daniel Zhengyu and Qian, Elizabeth and Stuart, Andrew M}, journal={arXiv preprint arXiv:2203.13181}, year={2022} }.
Description
The data set is generated to study different neural networks for AI + engineering. The data set contains simulation data for the following 4 benchmark problems 1) Navier stokes equation : NavierStokes_inputs.npy & NavierStokes_outputs.npy. 2) Helmholtz equation : Helmholtz_inputs.npy & Helmholtz_outputs.npy. 3) Structural mechanics equation : StructuralMechanics_inputs.npy & StructuralMechanics_outputs.npy. 4) Advection equation : Advection_inputs.npy & Advection_outputs.npy.
The data are stored as nx by ny by ndata arrays (2d problems) or nx by ndata arrays (1d problems).
Links
Where it is published
- CaltechDATA record data.caltech.edu/records/fp3ds-kej20 ↗
landing page · from data caltech edu
- DOI doi.org/10.22002/d1.20091 ↗
DOI / persistent id · from data caltech edu
Catalogue records · 1
- InvenioRDM API data.caltech.edu/api/records/fp3ds-kej20 ↗
metadata API · from data caltech edu
Topics
- Stated by source
- Computational complexity · Computational partial differential equations · Neural networks · Operator approximation · Surrogate modeling
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Neural networks · Social Science
- Inferred from text
- Simulation 75%
Provenance · 1 source records, 18 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| CaltechDATA | fp3ds-kej20 | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · data caltech edu | connector:data_caltech_edu@1.0.0 | |
| concepts[field].anzsrc:field:461104 | mapping · data caltech edu | vocabulary-mapper@1.0.0 | keywords['Neural networks'] |
| concepts[field].local:field:earth-environmental | mapping · data caltech edu | connector:data_caltech_edu@1.0.0 | |
| concepts[field].local:field:engineering | mapping · data caltech edu | connector:data_caltech_edu@1.0.0 | |
| concepts[field].local:field:humanities | mapping · data caltech edu | connector:data_caltech_edu@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · data caltech edu | connector:data_caltech_edu@1.0.0 | |
| concepts[field].local:field:social-science | mapping · data caltech edu | connector:data_caltech_edu@1.0.0 | |
| concepts[method].local:method:simulation | enrichment · data caltech edu | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[topic].invenio_subject:data_caltech_edu:computational-complexity | source · data caltech edu | connector:data_caltech_edu@1.0.0 | |
| concepts[topic].invenio_subject:data_caltech_edu:computational-partial-differential-equations | source · data caltech edu | connector:data_caltech_edu@1.0.0 | |
| concepts[topic].invenio_subject:data_caltech_edu:neural-networks | source · data caltech edu | connector:data_caltech_edu@1.0.0 | |
| concepts[topic].invenio_subject:data_caltech_edu:operator-approximation | source · data caltech edu | connector:data_caltech_edu@1.0.0 | |
| concepts[topic].invenio_subject:data_caltech_edu:surrogate-modeling | source · data caltech edu | connector:data_caltech_edu@1.0.0 | |
| description | source · data caltech edu | connector:data_caltech_edu@1.0.0 | /metadata/description |
| publication_date | source · data caltech edu | connector:data_caltech_edu@1.0.0 | |
| title | source · data caltech edu | connector:data_caltech_edu@1.0.0 | /metadata/title |
| updated_date | source · data caltech edu | connector:data_caltech_edu@1.0.0 | |
| version_label | source · data caltech edu | connector:data_caltech_edu@1.0.0 |