Maria Han Veiga

Maria Han Veiga works at the intersection of numerical methods for scientific computing and machine learning, developing and improving algorithms for simulating physical systems (such as fluid dynamics and astrophysics) and combining these with neural network techniques. Recent work includes teaching materials on machine learning fundamentals, methods for approximating functions with radial basis functions using neural networks, and high-order numerical schemes (like ADER and Spectral Difference methods) for solving differential equations.

Publication output has fluctuated over the last decade with no clear increase or decrease, showing periodic peaks (e.g., 2019, 2024) and dips (e.g., 2017, 2022), averaging around 3 publications per year over the last five years.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026