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Author |
Ferrer-Sanchez, A.; Martin-Guerrero, J.; Ruiz de Austri, R.; Torres-Forne, A.; Font, J.A. |
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Title |
Gradient-annihilated PINNs for solving Riemann problems: Application to relativistic hydrodynamics |
Type |
Journal Article |
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Year |
2024 |
Publication |
Computer Methods in Applied Mechanics and Engineering |
Abbreviated Journal |
Comput. Meth. Appl. Mech. Eng. |
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Volume |
424 |
Issue |
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Pages |
116906 - 18pp |
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Keywords |
Riemann problem; Euler equations; Machine learning; Neural networks; Relativistic hydrodynamics |
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Abstract |
We present a novel methodology based on Physics-Informed Neural Networks (PINNs) for solving systems of partial differential equations admitting discontinuous solutions. Our method, called Gradient-Annihilated PINNs (GA-PINNs), introduces a modified loss function that forces the model to partially ignore high-gradients in the physical variables, achieved by introducing a suitable weighting function. The method relies on a set of hyperparameters that control how gradients are treated in the physical loss. The performance of our methodology is demonstrated by solving Riemann problems in special relativistic hydrodynamics, extending earlier studies with PINNs in the context of the classical Euler equations. The solutions obtained with the GA-PINN model correctly describe the propagation speeds of discontinuities and sharply capture the associated jumps. We use the relative l(2) error to compare our results with the exact solution of special relativistic Riemann problems, used as the reference ''ground truth'', and with the corresponding error obtained with a second-order, central, shock-capturing scheme. In all problems investigated, the accuracy reached by the GA-PINN model is comparable to that obtained with a shock-capturing scheme, achieving a performance superior to that of the baseline PINN algorithm in general. An additional benefit worth stressing is that our PINN-based approach sidesteps the costly recovery of the primitive variables from the state vector of conserved variables, a well-known drawback of grid-based solutions of the relativistic hydrodynamics equations. Due to its inherent generality and its ability to handle steep gradients, the GA-PINN methodology discussed in this paper could be a valuable tool to model relativistic flows in astrophysics and particle physics, characterized by the prevalence of discontinuous solutions. |
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Address |
[Ferrer-Sanchez, Antonio; Martin-Guerrero, JoseD.] ETSE UV, Elect Engn Dept, IDAL, Avgda Univ S-N, Valencia 46100, Spain, Email: Antonio.Ferrer-Sanchez@uv.es |
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Publisher |
Elsevier Science Sa |
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Language |
English |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0045-7825 |
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Conference |
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Notes |
WOS:001221797400001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
6126 |
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Author |
Villaescusa-Navarro, F. et al; Villanueva-Domingo, P. |
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Title |
The CAMELS Multifield Data Set: Learning the Universe's Fundamental Parameters with Artificial Intelligence |
Type |
Journal Article |
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Year |
2022 |
Publication |
Astrophysical Journal Supplement Series |
Abbreviated Journal |
Astrophys. J. Suppl. Ser. |
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Volume |
259 |
Issue |
2 |
Pages |
61 - 14pp |
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Keywords |
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Abstract |
We present the Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) Multifield Data set (CMD), a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from more than 2000 distinct simulated universes at several cosmic times. The 2D maps and 3D grids represent cosmic regions that span similar to 100 million light-years and have been generated from thousands of state-of-the-art hydrodynamic and gravity-only N-body simulations from the CAMELS project. Designed to train machine-learning models, CMD is the largest data set of its kind containing more than 70 TB of data. In this paper we describe CMD in detail and outline a few of its applications. We focus our attention on one such task, parameter inference, formulating the problems we face as a challenge to the community. We release all data and provide further technical details at https://camels-multifield-dataset.readthedocs.io. |
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Address |
[Villaescusa-Navarro, Francisco; Nicola, Andrina; Spergel, David N.; Matilla, Jose Manuel Zorrilla; Shao, Helen] Princeton Univ, Dept Astrophys Sci, Peyton Hall, Princeton, NJ 08544 USA, Email: villaescusa.francisco@gmail.com |
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Publisher |
IOP Publishing Ltd |
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Language |
English |
Summary Language |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Edition |
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ISSN |
0067-0049 |
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Area |
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Expedition |
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Conference |
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Notes |
WOS:000780035300001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5194 |
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Permanent link to this record |
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Author |
Villaescusa-Navarro, F. et al; Villanueva-Domingo, P. |
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Title |
The CAMELS Project: Public Data Release |
Type |
Journal Article |
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Year |
2023 |
Publication |
Astrophysical Journal Supplement Series |
Abbreviated Journal |
Astrophys. J. Suppl. Ser. |
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Volume |
265 |
Issue |
2 |
Pages |
54 - 14pp |
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Keywords |
Cosmology; Hydrodynamical simulations; Astrostatistics; Galaxy formation |
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Abstract |
The Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4233 cosmological simulations, 2049 N-body simulations, and 2184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper, we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogs, power spectra, bispectra, Lya spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over 1000 catalogs that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz semianalytic model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies, and summary statistics. We provide further technical details on how to access, download, read, and process the data at . |
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Address |
[Villaescusa-Navarro, Francisco; Genel, Shy; Angles-Alcazar, Daniel; Hassan, Sultan; Pisani, Alice; Wong, Kaze W. K.; Coulton, William R.; Steinwandel, Ulrich P.; Spergel, David N.; Burkhart, Blakesley; Wandelt, Benjamin; Somerville, Rachel S.; Bryan, Greg L.; Li, Yin] Flatiron Inst, Ctr Computat Astrophys, 162 5th Ave, New York, NY 10010 USA, Email: camel.simulations@gmail.com |
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Publisher |
IOP Publishing Ltd |
Place of Publication |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0067-0049 |
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Expedition |
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Conference |
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Notes |
WOS:000964876300001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5525 |
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Permanent link to this record |
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Author |
Liang, J.; Singh, B.; McCutchan, E.A.; Dillmann, I.; Birch, M.; Sonzogni, A.A.; Huang, X.; Kang, M.; Wang, J.; Mukherjee, G.; Banerjee, K.; Abriola, D.; Algora, A.; Chen, A.A.; Johnson, T.D.; Miernik, K. |
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Title |
Compilation and Evaluation of Beta-Delayed Neutron Emission Probabilities and Half-Lives for Z > 28 Precursors |
Type |
Journal Article |
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Year |
2020 |
Publication |
Nuclear Data Sheets |
Abbreviated Journal |
Nucl. Data Sheets |
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Volume |
168 |
Issue |
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Pages |
1-116 |
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Keywords |
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Abstract |
We present a compilation and evaluation of experimental beta-delayed neutron emission probabilities (P-n) and half-lives (T-1/2) for known or potential beta-delayed neutron precursors with atomic number Z > 28 (Cu-73 – Fr-233). This article includes the recommended values of both of these quantities, together with a compilation of experimental measurements when available. Some notable cases, as well as proposed standards for beta-delayed neutron measurements are also discussed. Evaluated data has also been compared to systematics using three different approaches. The literature cut-off date for this work is August 15, 2020. |
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Address |
[Liang, J.; Singh, B.; Birch, M.; Chen, A. A.] McMaster Univ, Dept Phys & Astron, Hamilton, ON L8S 4M1, Canada, Email: balraj@mcmaster.ca |
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Corporate Author |
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Thesis |
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Publisher |
Academic Press Inc Elsevier Science |
Place of Publication |
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Editor |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0090-3752 |
ISBN |
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Expedition |
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Conference |
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Notes |
WOS:000575888800001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4560 |
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Permanent link to this record |
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Author |
Dimitriou, P. et al; Tain, J.L.; Algora, A. |
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Title |
Development of a Reference Database for Beta-Delayed Neutron Emission |
Type |
Journal Article |
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Year |
2021 |
Publication |
Nuclear Data Sheets |
Abbreviated Journal |
Nucl. Data Sheets |
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Volume |
173 |
Issue |
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Pages |
144-238 |
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Keywords |
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Abstract |
Beta-delayed neutron emission is important for nuclear structure and astrophysics as well as for reactor applications. Significant advances in nuclear experimental techniques in the past two decades have led to a wealth of new measurements that remain to be incorporated in the databases. We report on a coordinated effort to compile and evaluate all the available beta-delayed neutron emission data. The different measurement techniques have been assessed and the data have been compared with semi-microscopic and microscopic-macroscopic models. The new microscopic database has been tested against aggregate total delayed neutron yields, time-dependent group parameters in 6-and 8-group re-presentation, and aggregate delayed neutron spectra. New recommendations of macroscopic delayed-neutron data for fissile materials of interest to applications are also presented. |
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Address |
[Dimitriou, P.; Verpelli, M.] IAEA, NAPC Nucl Data Sect, A-1400 Vienna, Austria, Email: p.dimitriou@iaea.org |
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Corporate Author |
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Thesis |
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Publisher |
Academic Press Inc Elsevier Science |
Place of Publication |
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Editor |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0090-3752 |
ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
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Notes |
WOS:000647012500006 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4828 |
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Permanent link to this record |