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Professor Darve received his Ph.D. in Applied Mathematics at the Jacques-Louis Lions Laboratory in the Pierre et Marie Curie University, Paris, France. His advisor was Prof. Olivier Pironneau, and his Ph.D. thesis was entitled "Fast Multipole Methods for Integral Equations in Acoustics and Electromagnetics." He was previously a student at the Ecole Normale Supérieure, rue d'Ulm, Paris, in Mathematics and Computer Science. Prof. Darve became a postdoctoral scholar with Profs. Moin and Pohorille at Stanford and NASA Ames in 1999 and joined the faculty at Stanford University in 2001. He is a member of the Institute for Computational and Mathematical Engineering.Prof. Darve has received many awards, including the H. Julian Allen Award, NASA (2010), the Habilitation à Diriger des Recherches, France (2007), the Leslie Fox Prize in Numerical Analysis, IMA (2001), and the James H. Clark Faculty Scholar, Stanford University (2001).His research areas include numerical linear algebra, high-performance and parallel computing, GPGPU, machine learning for engineering, surrogate and reduced order modeling, stochastic inversing, and anomaly detection for manufacturing.
Professor Darve's research is focused on the development of numerical methods for high-performance scientific computing, numerical linear algebra, fast algorithms, parallel computing, anomaly detection, and machine learning with applications in engineering. In many engineering applications, the computational expense of simulating large and complex systems is very significant and in many instances beyond current computer capabilities. The Darve research group is developing innovative numerical techniques to reduce this computational expense and enable the simulation of complex systems over realistic timescales. Keywords: numerical linear algebra (fast linear solvers, fast QR factorization, eigenvalue solvers, applications in geoscience and electric power grid), physics-informed machine learning (inverse modeling using PhysML, auto-encoders, GAN for uncertainty in predictive and inverse modeling, Kriging and statistical inversing, applications in geoscience, fluid mechanics and computational mechanics), anomaly detection (GAN-based algorithms, self-supervised machine learning, applications with Ford and SLAC linear accelerator), reinforcement learning for engineering applications (optimal control, application in 3D metal printing).