New trends in mathematics for scientific machine learning

This editorial introduces a special issue devoted to recent developments in scientific machine learning. The issue brings together contributions that illustrate, from complementary viewpoints, how mathematical analysis, modeling, scientific computing, control, and learning are becoming increasingly intertwined in the study of complex systems. Beyond presenting the papers collected in this volume, the editorial aims to place them within a broader conceptual landscape and to highlight a number of emerging directions that, in our view, will shape the future of the field. The presentation is organized in three parts: first, the motivations and scientific context underlying the special issue, second, a brief overview of the contributions included in the volume, and third, a discussion of some challenging perspectives at the interface of mathematics, scientific computing, and artificial intelligence.

Authors

Institutions

Publication Details

Journal
Mathematical Models and Methods in Applied Sciences
Published
2026-04-17
DOI
https://doi.org/10.1142/s0218202526020033
Primary Topic
Gaussian Processes and Bayesian Inference
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

New trends in mathematics for scientific machine learning

Franco Brezzi, N. Bellomo, E. Zuazua
Mathematical Models and Methods in Applied Sciences
Gaussian Processes and Bayesian Inference
article

New trends in mathematics for scientific machine learning

Franco Brezzi, N. Bellomo, E. Zuazua
article en

Abstract

This editorial introduces a special issue devoted to recent developments in scientific machine learning. The issue brings together contributions that illustrate, from complementary viewpoints, how mathematical analysis, modeling, scientific computing, control, and learning are becoming increasingly intertwined in the study of complex systems. Beyond presenting the papers collected in this volume, the editorial aims to place them within a broader conceptual landscape and to highlight a number of emerging directions that, in our view, will shape the future of the field. The presentation is organized in three parts: first, the motivations and scientific context underlying the special issue, second, a brief overview of the contributions included in the volume, and third, a discussion of some challenging perspectives at the interface of mathematics, scientific computing, and artificial intelligence.

Mathematical Models and Methods in Applied Sciences
Universidad de Deusto (ES), Universidad de Granada (ES), Politecnico di Torino (IT), Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), Istituto Universitario di Studi Superiori di Pavia (IT), Istituto di Matematica Applicata e Tecnologie Informatiche (IT), Universidad Autónoma de Madrid (ES)
Quality Education
Openalex Percentile: Top 13%
Gaussian Processes and Bayesian Inference
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.