The work that won Deepak Dhar the Dirac Medal | Explained
The story so far: Theoretical physicist Deepak Dhar has been awarded the Dirac Medal for his contributions to statistical physics — an honour he is
The story so far: Theoretical physicist Deepak Dhar has been awarded the Dirac Medal for his contributions to statistical physics — an honour he is sharing with three other physicists, Bernard Derrida, Marc Mézard, and Haim Sompolinsky. The Dirac Medal is awarded by the International Centre for Theoretical Physics (ICTP) in Trieste, Italy, on the birthday of the late British physicist Paul A.M. Dirac, for significant contributions to theoretical physics. Dhar has spent most of his career in India. He is also the second Indian winner of the Dirac Medal; the first was string theorist Ashoke Sen in 2012. In physics circles, Dhar is known for his work on a concept called Abelian sandpiles. What is an Abelian sandpile? Imagine dropping grains of sand, one at a time, on a flat surface. The pile slowly grows until it gets steep enough. Then, sometimes new grains on the pile will shift it, maybe cause a modest slide, sometimes even trigger an avalanche. And it is hard to predict in advance which grain will trigger which size of event. In the late 1980s, physicists Per Bak, Chao Tang, and Kurt Wiesenfeld proposed that this behaviour could be a general principle underlying many phenomena in nature, from earthquakes to forest fires to the extinction of species.
They called it self-organised criticality. The term refers to a system that sometimes organises itself into a delicately balanced critical state where even small nudges can produce effects of any size, from trivial to significant. Dhar and his collaborators then developed the so-called Abelian sandpile model as a mathematical version of the sandpile idea. ‘Abelian’ means no matter in which sequence grains topple and cascade, the pile always settles into the same final shape. This was Dhar’s important insight — a property that lets physicists calculate exact answers and thus predict the final shape. The Abelian sandpile model has numerous applications fundamentally because it captures a very general kind of behaviour: of systems that build up some kind of instability until they can release it in unpredictable ways. Scientists have used it to model earthquakes, the spread of forest fires, bursts of activity in neural networks in the brain, and fluctuations in financial markets. Biographical sketch Dhar was born in 1951 in Pratapgarh, Uttar Pradesh. He studied at Allahabad University, got his master’s degree from IIT-Kanpur, and then went to the California Institute of Technology (Caltech) for his PhD, which he completed in 1978. While at Caltech, he was a teaching assistant to Richard Feynman.
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