Shock waves are phenomena occurring in compressible fluids such as gases in which very large difference in pressure can occur over a very small distance. A shock wave can be thought of as a very thin front across which a large pressure increase exists, with the front moving rapidly in the fluid medium in which it is created. The sound waves generated when we speak are essentially very weak shock waves. When explosives go off, the shock wave generated as a consequence of the explosion is responsible for the damage that happens to the surroundings.

Vehicles that re-enter earth’s atmosphere from space come in at very high speeds that are classified as hypersonic speeds. The speed may well exceed more than ten times the local speed of sound. Such high speed flows generate a shock in front of the re-entry vehicle which affects the heat flux on its surface. The heat flux on the surface of the re-entry vehicle is an extremely important design parameter, and an accurate prediction of this parameter is a critical task.

For more than 2 millennia, science has progressed primarily by experimental observations and development of theories. These two methods work perfectly in conjunction with one another in developing our understanding of the physical world around us. In the past few decades, a third significant methodology employing powerful computers and computational science for simulations has greatly added to our scientific capability and understanding.

The smoothed particle hydrodynamics (SPH) method is a general-purpose numerical method that can be used to simulate a wide variety of problems. These problems range from astrophysics, incompressible and compressible fluid dynamics, to structural dynamics problems. SPH is a particle-based method and works by representing continuous fields using a collection of moving particles. The method does not depend on a fixed mesh and therefore works well for complex geometries, free-surface, and multi-physics problems.

India supports around 17.5% of the world’s population on a mere 2.4% of the Earth’s surface prone to global and regional climate change effects. Scientific research has largely focused on using models as inseparable components of climate studies. For a genuine understanding and realistic representation by which land surface processes influences climate, the role of land surface models (LSM) can never be overstated.

The road accident report (2014) published by the road transport and highways ministry, reports 6,672 deaths in accidents caused due to bad roads. Currently, road authorities manually monitor long stretches of roads at regular time intervals to ascertain the presence and locations of road anomalies. This is evidently a tedious process, which often leads to delayed road repairs, whose severity increases with time.

SAFE (Smart Authenticated Fast Exams) is a smart-phone app based system to conduct easy online-exams in proctored venues. Today, smart-phones or other electronic devices are explicitly disallowed in exam venues, due to the obvious possibilities of misuse for cheating. SAFE brings the smart-phone revolution into the exam hall.

Online repositories contain millions of 3D shapes, providing data for a wide range of data-driven 3D modeling interfaces. Such interfaces facilitate, accelerate and democratise computer-aided design and 3D content creation. By automatically learning design rules and structural principles from training data, these interfaces allow even novice and casual users to design complex and functional objects.

Many real world phenomena, like fluid flows, combustion, and garment drapes demonstrate astonishing complexity of structure, movement and appearance. Efficient and accurate simulation and animation of these play a crucial part in many applications ranging from scientific visualisation to advertising, from computer aided design to retail and from entertainment to medicine.

Our group’s research is motivated by the goal of creating intelligent agents, especially ones that can learn. In pursuit of this goal, we consider questions from a wide variety of topics. Central to our investigation is reinforcement learning (RL), which is a general paradigm for an agent, through trial and error, to discover actions that maximise its long-term gain. RL finds application in a variety of domains, including game-playing, stock-trading, medical decision-making, and environmental preservation.