Sergio Rojas

Sergio Rojas is a Senior Lecturer in the Applied and Computational Mathematics section at Monash University's School of Mathematics. His expertise spans numerical analysis, scientific computing, and mathematical modeling, focusing on residual minimization-based methods for solving complex partial differential equations. He holds a PhD and MSc in Engineering Sciences from Pontificia Universidad Católica de Chile, a Master's in Mathematics from the University of Pavia, and a Bachelor's in Mathematics from Pontificia Universidad Católica de Valparaíso (PUCV).

Education:

• PhD and MSc in Engineering Sciences, Pontificia Universidad Católica de Chile
• Master's in Mathematics, University of Pavia, Italy
• Bachelor's in Mathematics, PUCV, Chile

Research Interests: Dr. Rojas develops advanced numerical methods including Finite Element, Discontinuous Galerkin, and Physics-Informed Neural Networks. His work emphasizes robust algorithms for complex PDEs, with applications in computational fluid dynamics, geophysics, and solid mechanics. Recent studies explore adaptive stabilization techniques and solver optimization for parallel architectures.

Article Trends (2023-2025): Recent publications highlight robust neural network approaches for PDEs, adaptive finite element methods, and computational efficiency in parallel solvers. Key themes include residual minimization, stability analysis, and integration of machine learning in numerical simulations.

Supervision: Accepts undergraduate, master’s, and PhD students in numerical analysis and scientific computing. Current PhD projects include variational physics-informed schemes and residual minimization methods.

Labs/Teams: Collaborates on interdisciplinary projects involving computational mathematics and engineering applications, though specific lab affiliations are not detailed in the provided text.