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Abstract The DESI Milky Way Survey (MWS) will observe ≥8 million stars between 16 < r < 19 mag, supplemented by observations of brighter targets under poor observing conditions. The survey will permit an accurate determination of stellar kinematics and population gradients, characterize diffuse substructure in the thick disk and stellar halo, enable the discovery of extremely metal-poor stars and other rare stellar types, and improve constraints on the Galaxy’s 3D dark matter distribution from halo star kinematics. MWS will also enable a detailed characterization of the stellar populations within 100 pc of the Sun, including a complete census of white dwarfs. The target catalog from the preliminary selection described here is public (Available at https://data.desi.lbl.gov/public/ets/target/catalogs/and detailed at https://desidatamodel.readthedocs.io).
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Applications of physics informed neural operators
Abstract We present a critical analysis of physics-informed neural operators (PINOs) to solve partial differential equations (PDEs) that are ubiquitous in the study and modeling of physics phenomena using carefully curated datasets. Further, we provide a benchmarking suite which can be used to evaluate PINOs in solving such problems. We first demonstrate that our methods reproduce the accuracy and performance of other neural operators published elsewhere in the literature to learn the 1D wave equation and the 1D Burgers equation. Thereafter, we apply our PINOs to learn new types of equations, including the 2D Burgers equation in the scalar, inviscid and vector types. Finally, we show that our approach is also applicable to learn the physics of the 2D linear and nonlinear shallow water equations, which involve three coupled PDEs. We release our artificial intelligence surrogates and scientific software to produce initial data and boundary conditions to study a broad range of physically motivated scenarios. We provide thesource code, an interactivewebsiteto visualize the predictions of our PINOs, and a tutorial for their use at theData and Learning Hub for Science.
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- Award ID(s):
- 1931561
- PAR ID:
- 10414382
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Machine Learning: Science and Technology
- Volume:
- 4
- Issue:
- 2
- ISSN:
- 2632-2153
- Page Range / eLocation ID:
- Article No. 025022
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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