The DOI matches its Crossref record.
Mathematical Methods for Physicists
Abstract
Mathematical Methods for Physicists, Third Edition provides an advanced undergraduate and beginning graduate study in physical science, focusing on the mathematics of theoretical physics. This edition includes sections on the non-Cartesian tensors, dispersion theory, first-order differential equations, numerical application of Chebyshev polynomials, the fast Fourier transform, and transfer functions. Many of the physical examples provided in this book, which are used to illustrate the applications of mathematics, are taken from the fields of electromagnetic theory and quantum mechanics. The He
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
“Arfken [1985] provides an introduction to calculus of variations and Green’s functions.”
Quasinormal modes of black holes and black branes
Berti, E., Cardoso, V., & Starinets, A. O. (2009) · Classical and Quantum Gravity · doi:10.1088/0264-9381/26/16/163001 · Cited by 2,095
Solution of population balance equations using the direct quadrature method of moments
Marchisio, D. L., & Fox, R. O. (2005) · Journal of Aerosol Science · doi:10.1016/j.jaerosci.2004.07.009 · Cited by 803
The Effects of Adding Noise During Backpropagation Training on a Generalization Performance
An, G. (1996) · Neural Computation · doi:10.1162/neco.1996.8.3.643 · Cited by 475
On self-propulsion of micro-machines at low Reynolds number: Purcell's three-link swimmer
BECKER, L. E., KOEHLER, S. A., & STONE, H. A. (2003) · Journal of Fluid Mechanics · doi:10.1017/s0022112003005184 · Cited by 337
Metadata
- Authors
- Arfken, G., & Mullin, A. A.
- Venue
- American Journal of Physics
- Published
- 1967-11-01
- Volume
- 35
- Issue
- 11
- Pages
- 1097-1098
- ISSN
- 0002-9505 (ISSN-L)
- 1943-2909
- OpenAlex
- W2049387919
- Cited by
- 2,688
- References
- 0
- FWCI
- 10.26
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Mathematical Methods for Physicists. https://app.cochat.ai/papers/10.1119/1.1973757
- The paper
- Arfken, G., & Mullin, A. A. (1967). Mathematical Methods for Physicists. American Journal of Physics. https://doi.org/10.1119/1.1973757
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
