Researchers propose a spatio-temporal model for high-resolution wind forecasting in Saudi Arabia using Echo State Networks and stochastic partial differential equations. The model reduces spatial information via energy distance, captures dynamics with a sparse recurrent neural network, and reconstructs data using a non-stationary stochastic partial differential equation approach. The model achieves more accurate forecasts of wind speed and energy, potentially saving up to one million dollars annually compared to existing models.
KAUST Professor Peter Markowich has been named a 2022 Fellow of the American Mathematical Society (AMS). He is recognized for contributions to partial differential equations, particularly the mathematical and numerical analysis of dispersive equations. Markowich applies differential mathematics to disciplines such as physics, AI, biology and engineering, including research on leaf venation patterns. Why it matters: This recognition highlights KAUST's strength in applied mathematics and its faculty's contributions to both theoretical and interdisciplinary research.
KAUST Professor of Applied Mathematics and Computational Science, Dr. Peter Markowich, has been named a 2020 Fellow to the European Academy of Sciences. This recognizes his work in the mathematical and numerical analysis of partial differential equations. Markowich joined KAUST in 2011 and has contributed to over 270 projects worldwide. Why it matters: This honor brings recognition to KAUST's faculty and highlights the university's contribution to advanced mathematical research with applications across science and engineering.