Tishampati Dhar
Tisham is a Senior Engineer at CSIRO Space and Astronomy division. Previously he held similar roles in various government agencies, private sector companies and startups. He has been pushing pixels since 2004 when he got started using medical imaging devices in Singapore. Since then he has had the priviledge of working with various space agencies such as NASA, DLR, JAXA, CNES etc. and lived through the explosion of publicly available satellite imagery.
Session
Water quality modeling plays a crucial role in managing aquatic ecosystems. Those models are based on Computational Fluid Dynamics (CFD), the science of using numerical analysis and data structures to solve many processes influencing fluid behavior. CFD requires many complex mathematical representations through various ODE and DPE solvers, and has traditionally been written in Fortran or C++, mostly for their speed in doing massively parallel computations, such as handling array operations and object-oriented features. As such, those codebases have a long-standing legacy in scientific computing, and many established CFD codes are written in them. However, as time goes on, we witness the popularity of a new generation of codebases such as Python, which stems from its simplicity, versatility, and strong community. Extensive libraries and frameworks make Python a popular choice for many developers and scientists alike. It is, however, not a preferred language for core CFD code due to performance limitations and its difficulties with parallelization.
This talk explores modernizing a 25+ year-old C++ CFD model, essential for simulating lake conditions, particularly for predicting temperature variations and algal bloom dynamics, by wrapping it with C-interop for seamless Python interoperability.
Beyond integration, we leverage Optuna, a powerful hyperparameter optimization framework, to fine-tune models efficiently, transitioning from manual parameter tuning on a laptop to a distributed, scalable workflow powered by Dask Distributed and JupyterHub. This transformation enables automated hyperparameter optimization across many lakes in Australia, helping researchers investigate trends in tuning parameters and derive deeper environmental insights.