Applied mathematics and machine learning are converging at pace, giving rise to scientific machine learning and data-driven modelling across PDEs, inverse problems and control. This convergence is reshaping methods and applications in science and engineering.  

Key current directions

· Physics-informed learning for PDEs and dynamical systems.

· Neural operators and fast surrogates for simulation.

· Data-driven modelling, inverse problems and imaging.

· Optimisation, control and “learning to optimise”.

· Explainable/trustworthy AI in high-stakes settings.

· Cross-disciplinary and industrial applications.

Workshop information

Title: Recent Trends in Applied Mathematics and Machine Learning 2026

Dates & sequence: 

16 September 2026: Registration

17 – 19 September 2026: Workshop

Venue: 

Room 209, Zhengxin Building, Jilin Univeristy

Location: No. 3003 Qianjin Avenue, High and New Tech District, Changchun, Jilin, 130000