Journal of Computational Physics
Overview
The Journal of Computational Physics is a leading scientific journal that focuses on the development and application of computational methods in physics. It publishes high-quality research articles that explore numerical techniques, algorithms, and simulations to solve complex problems in various areas of physics, including fluid dynamics, solid mechanics, quantum mechanics, and astrophysics. The journal serves as a platform for researchers to exchange ideas and advancements in computational physics, fostering interdisciplinary collaborations and pushing the boundaries of scientific knowledge.
Details
Details
Abbr.
J Comput Phys
Start
1966
End
Continuing
Frequency
Monthly, <May 1992->
p-ISSN
0021-9991
Country
United States
Language
English
Metrics
Metrics
h-index / Ranks: 239
288
SJR / Ranks: 1608
1679
CiteScore / Ranks: 2299
7.90
JIF / Ranks: 2178
4.1
Recent Articles
1.
Hao W, Lee S, Xu X, Xu Z
J Comput Phys
. 2024 Nov;
521(Pt 2).
PMID: 39583931
The Allen-Cahn equation is a fundamental model for phase transitions, offering critical insights into the dynamics of interface evolution in various physical systems. This paper investigates the stability and robustness...
2.
Cohen B, Beykal B, Bollas G
J Comput Phys
. 2024 Sep;
514.
PMID: 39309523
A novel framework is proposed that utilizes symbolic regression via genetic programming to identify free-form partial differential equations from scarce and noisy data. The framework successfully identified ground truth models...
3.
Yildiran I, Beratlis N, Capuano F, Loke Y, Squires K, Balaras E
J Comput Phys
. 2024 Jun;
510.
PMID: 38912295
Immersed boundary methods have seen an enormous increase in popularity over the past two decades, especially for problems involving complex moving/deforming boundaries. In most cases, the boundary conditions on the...
4.
Lahouel K, Wells M, Rielly V, Lew E, Lovitza D, Jedynak B
J Comput Phys
. 2024 May;
507.
PMID: 38745873
Learning nonparametric systems of Ordinary Differential Equations (ODEs) from noisy data is an emerging machine learning topic. We use the well-developed theory of Reproducing Kernel Hilbert Spaces (RKHS) to define...
5.
Gruninger C, Barrett A, Fang F, Gregory Forest M, Griffith B
J Comput Phys
. 2024 May;
506.
PMID: 38737497
We present and analyze a series of benchmark tests regarding the application of the immersed boundary (IB) method to viscoelastic flows through and around non-trivial, stationary geometries. The IB method...
6.
Rydquist G, Esmaily M
J Comput Phys
. 2024 Apr;
420.
PMID: 38595734
The cost of tracking Lagrangian particles in a domain discretized on an unstructured grid can become prohibitively expensive as the number of particles or elements grows. A major part of...
7.
Zheng H, Huang Y, Huang Z, Hao W, Lin G
J Comput Phys
. 2024 Jan;
500.
PMID: 38283188
Due to the complex behavior arising from non-uniqueness, symmetry, and bifurcations in the solution space, solving inverse problems of nonlinear differential equations (DEs) with multiple solutions is a challenging task....
8.
Chen D
J Comput Phys
. 2023 Dec;
494.
PMID: 38098855
Kernel functions play an important role in a wide range of scientific computing and machine learning problems. These functions lead to dense kernel matrices that impose great challenges in computational...
9.
Zhao S, Ijaodoro I, McGowan M, Alexov E
J Comput Phys
. 2023 Dec;
497.
PMID: 38045553
The Poisson-Boltzmann (PB) equation governing the electrostatic potential with a unit is often transformed to a normalized form for a dimensionless potential in numerical studies. To calculate the electrostatic free...
10.
Strahan J, Finkel J, Dinner A, Weare J
J Comput Phys
. 2023 Jun;
488.
PMID: 37332834
Estimating the likelihood, timing, and nature of events is a major goal of modeling stochastic dynamical systems. When the event is rare in comparison with the timescales of simulation and/or...