معرفی
Benjamin Peherstorfer is an Associate Professor at the Courant Institute of Mathematical Sciences, New York University. His research focuses on computational mathematics, machine learning, and scientific computing, with an emphasis on nonlinear model reduction, uncertainty quantification, and multifidelity methods. He holds editorial roles at journals such as Journal of Scientific Computing and SIAM Journal on Scientific Computing.
Peherstorfer co-organizes workshops on topics like digital twins and fusion energy, reflecting his commitment to advancing interdisciplinary computational science. He has advised students like Jules Berman, who received the Harold Grad Memorial Prize. His work integrates machine learning with traditional numerical methods to address challenges in high-dimensional systems and real-time simulations.
Notable contributions include developing Neural Galerkin schemes for solving evolution equations and advancing multifidelity Monte Carlo methods to accelerate uncertainty propagation. His research bridges theoretical foundations with practical applications, such as wildfire-atmosphere coupling and plasma confinement in stellarators.
- Key Roles:
- Secretary of the SIAM Activity Group on Computational Science and Engineering
- Co-organizer of the Long Program on Multi-Fidelity Methods for Fusion Energy (IPAM, 2026)
- Main Research Themes:
- Reduced-order modeling
- Data-driven operator inference
- Adaptive sampling algorithms
