Scientific Machine Learning and Operations (STARS) at the Department of Computational and Data Sciences is under the Division of Interdisciplinary Research at Indian Institute of Science Bangalore, India.

We develops accurate, efficient and robust parallel numerical (finite element) schemes for solving partial differential equations (PDEs),  Scientific Machine Learning and High Performance Computing.

Please visit our in-house finite element package ParMooN.


Finite Element Analysis:
finite element methods for the solution of PDEs and surface PDEs
multigrid methods, ALE approach for moving meshes
variational multiscale methods based Turbulence modelling for fluid flows.
Higher order discontinuous Galerkin schemes for hyperbolic PDE's
Scientific Machine Learning
ML based stabilisation schemes for Singularly perturbed PDE's
Physics Informed Neural Networks for Fluid flows
Reduced order modelling for fluid flows
Digital Shadowing using ROM and PINNs for realtime flow prediction
High Performance Computing:
ParMooN, an open-source finite element software development
GPU-Accelerated Algebraic Multigrid solvers
Computational Fluid Dynamics:
fluid-structure interaction problems, ship hydrodynamics
turbulent flows with moving/deforming solid bodies
Bio-medical Applications:
computational models for particle deposition in human air pathway
biophysical model of cancer invasion