Samila Bandara
Computational Scientist at Digital Development @Arup
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WORK HISTORY
Computational Scientist at Digital Development @Arup
London, GB
Writing the FE solver behind Oasys GOFER - next generation web-based geotechnical analysis. The work is comprised of deriving the mathematical formulation, C++ implementation, testing and validation. Gofer is capable of solving 2D geotechnical problems (e.g. deep excavations, slope stability, seepage problems) and contains drained/undrained solver, seepage solver and consolidation solver with soil-structure interfaces.
EDUCATION
University of Moratuwa
B.Sc. (Hons), Civil Engineering
University of Cambridge
PhD, Geotechnical Engineering (Computational Geomechanics)
SKILLS
ABOUT SAMILA BANDARA
Computational Geomechanics specialist with extensive experience in numerical simulations (FEM) & scientific software development related to advanced civil and geotechnical engineering projects. Fluent in FE software packages such as Ls-Dyna, Plaxis and implementation of constitutive models in these platforms. Highly skilled in formulating FE algorithms (e.g. explicit/implicit solvers & non-linear material models) and implementing these algorithm using coding languages such as C++, Python, JavaScript and Fortran. Skilled in software development processes such as Agile, test driven development and coding standards.Possesses in-depth knowledge in theory and formulation of FE method and large deformation modelling techniques material point method (MPM) & SPH, and highly skilled in deriving and implementing mathematical formulas and algorithms related to these methods. Was the founder and main developer of MPM codes at Cambridge University and EPFL.Specialties: Computational Geomechanics, Computational modelling of fluids and solids, scientific code development, Meshfree methods (MPM, SPH), finite element modelling (with Ls-Dyna, Plaxis, SAFE, Abaqus), Landslide modelling, Geotechnical design, Soil constitutive modelling. Fluent in programming in C++, Python, Javascript, Visual Basic. Currently exploring Machine learning techniques for Engineering applications.
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