petsc

On the Use of "Conventional" Unconstrained Minimization Solvers for Training Regression Problems in Scientific Machine Learning

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B9 L2 R2325

This talk introduces PETScML, a framework leveraging traditional second-order optimization solvers for use within scientific machine learning, demonstrating improved generalization capabilities over gradient-based methods routinely adopted in deep learning.

Device accelerated solvers for PDEs. Current status and future perspectives

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B5 L5 R5220

In the next months, KAUST expects to place into service Shaheen-3, a supercomputer with a GPU partition whose planned 1 Exaflop/s HPL AI capability would rank it in the Top 6 globally if delivered today.

Lisandro Dalcin

Lisandro Dalcin obtained a degree in Electromechanical Engineering from National Technological University, Concepción del Uruguay, Argentina in 2001 and a PhD degree in Engineering Sciences, Computational Mechanics from National University of the Littoral, Santa Fe, Argentina in 2008. Research Interest Lisandro Dalcin's research interest include ​​Parallel and High Performance Computing, Scientific Software Development, Finite Element Methods and Computational Fluid Dynamics.​ Education Profile ​​PhD in Engineering Sciences, Computational Mechanics, National University of the Littoral, Santa