Editorial: Uncertainty quantification in nuclear physics

被引：1

作者：

Piarulli, Maria ^{[1
]}

Epelbaum, Evgeny ^{[2
]}

Forssen, Christian ^{[3
]}

机构：

[1] Washington Univ, Phys Dept, St Louis, MO 63130 USA

[2] Ruhr Univ Bochum, Inst Theoret Phys 2, Bochum, Germany

[3] Chalmers Univ Technol, Dept Phys, Gothenburg, Sweden

来源：

FRONTIERS IN PHYSICS | 2023年 / 11卷

关键词：

nuclear physics; uncertainty quantification; Bayesian methods; emulators; effective field theory; ab initio; many-body physics;

D O I：

10.3389/fphy.2023.1270577

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

引用

页数：2

共 50 条

[41] EPISTEMIC UNCERTAINTY QUANTIFICATION OF FLOOR RESPONSES FOR A NUCLEAR REACTOR BUILDING
Choi, Byunghyun
Nishida, Akemi
Li, Yinsheng
Muramatsu, Ken
Takada, Tsuyoshi
PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON NUCLEAR ENGINEERING, 2018, VOL 2, 2018,
[42] Fast uncertainty quantification of spent nuclear fuel with neural networks
Alba, Arnau
Adelmann, Andreas
Munster, Lucas
Rochman, Dimitri
Boiger, Romana
ANNALS OF NUCLEAR ENERGY, 2024, 196
[43] Introducing Robust Statistics in the Uncertainty Quantification of Nuclear Safeguards Measurements
Cerasa, Andrea
ENTROPY, 2022, 24 (08)
[44] Uncertainty quantification of spent nuclear fuel with multifidelity Monte Carlo
Alba, Arnau
Adelmann, Andreas
Rochman, Dimitri
ANNALS OF NUCLEAR ENERGY, 2024, 211
[45] Importance of uncertainty quantification in nuclear fuel behaviour modelling and simulation
Boulore, A.
NUCLEAR ENGINEERING AND DESIGN, 2019, 355
[46] VERIFICATION, VALIDATION AND UNCERTAINTY QUANTIFICATION OF A NUCLEAR THERMAL HYDRAULICS CODE
Aydogan, Fatih
PROCEEDINGS OF THE ASME INTERNATIONAL MECHANICAL ENGINEERING CONGRESS AND EXPOSITION, 2013, VOL 6B, 2014,
[47] PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics
Daw, Arka
Maruf, M.
Karpatne, Anuj
KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, 2021, : 237 - 247
[48] Effect of mesh refinement on the solution of the inverse uncertainty quantification problem for transient physics
Abu Saleem, Rabie A.
Alsafadi, Farah R.
Al-Abidah, Nadeen
PROGRESS IN NUCLEAR ENERGY, 2022, 152
[49] Physics-informed variational inference for uncertainty quantification of stochastic differential equations
Shin, Hyomin
Choi, Minseok
JOURNAL OF COMPUTATIONAL PHYSICS, 2023, 487
[50] Integrating Generative and Physics-Based Models for Ptychographic Imaging with Uncertainty Quantification
Ekmekci, Canberk
Bicer, Tekin
Di, Zichao Wendy
Deng, Junjing
Cetin, Mujdat
arXiv,

← 1 2 3 4 5 →