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Glad @PrinSciAdvGoI alluded to the importance of large-scale (computational and mathematical) modeling projects; traditionally India has lagged in these aspects. Computational modeling complements theory, experiments and observations, and illuminates complex systems in nature.
Climate, weather and space sciences, disease outbreaks, ecological sciences, traffic and water management, design of novel materials, drug discovery, cognition and human behavior, cryptography, finance and markets are some of the important areas that benefit from modelling.
However, large-scale modeling pursuits critically depend on adequate computational resources across institutions and a proactive intent to support modelers. We haven't done that very well, not quite knowing where the modeler fits in between the theorist and the experimentalist.
E.g., individuals often get a start-up grant of a few crores to buy instruments and machines (made and perfected in another country) and then play catch-up to the science perfected in another country! We don't try to build and perfect those instruments, however.
Likewise, for far too long India was nowhere in the computational modeling scene and indeed we relied on models developed by other countries (and their generosity) for our climate and weather (including rainfall) forecasts! We did not invest on good modelers early on.
The US and UK did and they led the world in climate and weather modeling. They led the world in understanding our space environment and the Universe. Other countries caught on. India has been slow.
Finally our investments in super computers and modeling expertise in climate sciences is paying off. We can make reasonable monsoon and weather forecasts that helps the agricultural sector, saves life and prepares the nation for eventualities such as #fani cyclone
But are we doing enough in the modeling game? How about ecological impacts, the spread of diseases, drug discovery, financial markets, the human brain, our space environment? A handful of modelers scattered in a country of billions.
I can speak with some claim of familiarity about fields close to my own: astronomy and astrophysics, and space sciences. Today when we talk about our role in LIGO our stories still carry the images of the wonderful simulations of inspiralling Black Holes done in the US.
When we highlight our involvement in the Thirty Meter Telescope and the other worlds we shall explore, we use simulations and images created elsewhere for our outreach, exhibitions and talks. Because we haven't made any investetment in simulation expertise.
We send missions to other planets and the moon but haven't made sustained investments in modelers who can simulate their atmospheric dynamics and understand how the space environment has shaped these planets over billions of years. So we get data but not the big picture!
Touching the tiny tail but not knowing the mighty Elephant that swings it, or collecting a few scattered photons in a well not knowing the wonder of the star from whence it comes are opportunities lost and signs of great shortsightedness.
Large-scale, global computational models of complex systems add valuable context, and provide the big picture that aid in the interpretation of data and in making sense of causality when multiple variables, and non-linearities are involved.
Computational models of complex systems are like a complicated machine. They take deep theoretical understanding, coding abilities, patience and time to develop but become critical tools to explore our physical and living world.
Therefore when investing in experiments, observatories, telescopes, machines and basic sciences, let's not forget the power of numerical simulations and modelling and invest in a thriving community of modelers who can tackle complex scientific problems of relevance to the nation.
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