Question 1

Which countries lead Computational Mathematics research?

Accepted Answer

On research quality in 2022-now, the United States and China rank highest in Computational Mathematics. By volume, China and the United States publish the most (934 and 454 works in 2022-now).

Question 2

Which institutions lead Computational Mathematics research?

Accepted Answer

By volume in 2022-now, Chinese Academy of Sciences publishes the most Computational Mathematics research, followed by Centre National de la Recherche Scientifique and University of Electronic Science and Technology of China.

Question 3

Who are the leading researchers in Computational Mathematics?

Accepted Answer

Counting only their work in Computational Mathematics, Yimin Wei, Liqun Qi and Michael K. Ng have the most papers in the world top 10% for their subfield and year beyond the 10% expected.

Question 4

Where is Computational Mathematics research done?

Accepted Answer

The largest centres of Computational Mathematics research in 2022-now are Beijing (China), Shanghai (China), Xi'an (China) and Chengdu (China).

Question 5

Where is the best place to study Computational Mathematics?

Accepted Answer

Among universities, judged by research, Southwest University, University of Electronic Science and Technology of China and Fudan 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.

Question 6

Is Computational Mathematics research growing?

Accepted Answer

Output in 2022-2025 was 24% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Tensor decomposition and applications.

Question 7

What are the most cited papers in Computational Mathematics?

Accepted Answer

The most cited paper in Computational Mathematics is "Tensor Decompositions and Applications" (Tamara G. Kolda, Brett W. Bader, 2009), with 10,717 citations. Next come "Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions" (4,178) and "Generalized Procrustes Analysis" (3,317).