Numerical methods in inverse problems
Numerical methods in inverse problems is a research topic within Mathematical Physics. Science Explorer counts 31k research works in it since 1950. 15.6% of them reached the world's top 10% most cited for their field and year.
This cluster of papers focuses on the mathematical theory and computational methods for solving inverse problems in various fields, including mathematical physics and imaging. It covers topics such as regularization methods, ill-posed problems, electrical impedance tomography, Calderón's problem, boundary value problems, inverse scattering theory, Tikhonov regularization, transmission eigenvalues, and numerical methods.
- Inverse Problems
- Regularization Methods
- Ill-Posed Problems
- Electrical Impedance Tomography
- Calderón's Problem
- Boundary Value Problem
- Inverse Scattering Theory
- Tikhonov Regularization
- Transmission Eigenvalues
- Numerical Methods
- Research works
- 31k fractional, since 1950
- In the world top 10%
- 4.8k per year above
- Top-10% rate
- 15.6% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- +7% the tick is no change
Which countries lead Numerical methods in inverse problems research?
By volume, China and the United States publish the most (942 and 461 works in 2022–2025).
By volume, 2022–2025
- 1 China 942 works
- 2 United States 461 works
- 3 Russia 450 works
- 4 France 262 works
- 5 Italy 204 works
- 6 Germany 189 works
- 7 India 179 works
- 8 Japan 97 works
- 9 United Kingdom 86 works
- 10 Uzbekistan 77 works
How concentrated that is
The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.
Shares of the rows listed above, not of the whole node.
Which institutions lead Numerical methods in inverse problems research?
By volume in 2022–2025, Centre National de la Recherche Scientifique publishes the most Numerical methods in inverse problems research, followed by Lomonosov Moscow State University and St Petersburg University.
By volume, 2022–2025
- 1 Centre National de la Recherche ScientifiqueFrance 39 works
- 2 Lomonosov Moscow State UniversityRussia 39 works
- 3 St Petersburg UniversityRussia 24 works
- 4 Harbin Institute of TechnologyChina 19 works
- 5 Academy of Sciences Republic of UzbekistanUzbekistan 19 works
- 6 Tsinghua UniversityChina 18 works
- 7 Bukhara State UniversityUzbekistan 18 works
- 8 Beijing Institute of TechnologyChina 18 works
- 9 Sobolev Institute of MathematicsRussia 16 works
- 10 Russian Academy of SciencesRussia 16 works
Who are the leading researchers in Numerical methods in inverse problems?
The most-cited researchers publishing on Numerical methods in inverse problems include Stanley Osher and Yonina C. Eldar.
- 1 Stanley Osher United States 8k citations
- 2 Yonina C. Eldar Israel 4k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Numerical methods in inverse problems research done?
The largest centres of Numerical methods in inverse problems research in 2022–2025 are Moscow (Russia), Beijing (China), Paris (France) and Shanghai (China). Among places with at least 20 works in it, it is an unusually large share of all research in Bukhara.
Largest cities, 2022–2025
- 1 Moscow Russia 161 works
- 2 Beijing China 157 works
- 3 Paris France 76 works
- 4 Shanghai China 61 works
- 5 Nanjing China 53 works
- 6 Saint Petersburg Russia 43 works
- 7 Novosibirsk Russia 42 works
- 8 Tashkent Uzbekistan 41 works
- 9 Xi'an China 39 works
- 10 Wuhan China 39 works
Where it is the local speciality
- BukharaUZ · 20.2 works33×
Location quotient: how much more of its research is in Numerical methods in inverse problems than the world average.
Where is the best place to study Numerical methods in inverse problems?
Among universities, judged by research, Université Sultan Moulay Slimane, Saratov State University and Cadi Ayyad University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Université Sultan Moulay SlimaneMorocco | 69.5 | 33.8% | 22.4× | 14 | — |
| 2 | Saratov State UniversityRussia | 65.3 | 32.2% | 32.9× | 12 | +45.8% |
| 3 | Cadi Ayyad UniversityMorocco | 62.6 | 18.8% | 13.8× | 11 | +337.3% |
| 4 | Al-Farabi Kazakh National UniversityKazakhstan | 60.3 | 12.8% | 16.2× | 13 | +264.1% |
| 5 | Lomonosov Moscow State UniversityRussia | 59.7 | 6.3% | 10.3× | 38 | +50.3% |
| 6 | University of JyväskyläFinland | 57.8 | 22.2% | 16.1× | 13 | +7.0% |
| 7 | Northwest Normal UniversityChina | 56.0 | 20.3% | 14.1× | 15 | +51.8% |
| 8 | Angel Kanchev University of RuseBulgaria | 55.3 | 23.3% | 42.2× | 10 | — |
| 9 | Peoples' Friendship University of RussiaRussia | 55.2 | 16.4% | 8.2× | 12 | +256.3% |
| 10 | Penza State UniversityRussia | 54.9 | 11.2% | 46.8× | 13 | +219.5% |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Numerical methods in inverse problems research growing?
Output in 2018–2022 was 7% higher than in 2013–2017, peaking in 2024. The fastest-growing topics are Numerical methods in inverse problems.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Which topics inside it are moving
Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.