Browsing Department of Computational and Data Sciences (CDS) by Subject "Partial Differential Equations"

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Data Driven Stabilization Schemes for Singularly Perturbed Differential Equations

Yadav, Sangeeta

This thesis presents a novel way of leveraging Artificial Neural Network (ANN) to aid conventional numerical techniques for solving Singularly Perturbed Differential Equation (SPDE). SPDEs are challenging to solve with ...
Improving hp-Variational Physics-Informed Neural Networks: A Tensor-driven Framework for Complex Geometries, and Singularly Perturbed and Fluid Flow Problems

Anandh, Thivin

Scientific machine learning (SciML) combines traditional computational science and physical modeling with data-driven deep learning techniques to solve complex problems. It generally involves incorporating physical ...
Scalable Asynchrony-Tolerant PDE Solver for Multi-GPU Systems

Matthew, Vinod Jacob

Partial differential equations (PDEs) are used to model various natural phenomena and engineered systems. At conditions of practical interest, these equations are highly non-linear and demand massive computations. Current ...