There’s a really fundamental law in physics telling us that entropy always increases. She said, “Entropy is the measure of the level of order or the level of information. There are a lot of times where they use science jargon and it’s just jargon with no content,” she said.ĭe Rham also explained the concept of entropy. Compared to previous movies, like Interstellar, the gap is maybe bigger. The Los Angeles Times spoke with Claudia De Rham, a theoretical physicist from Imperial College London, and asked her if the science in the film holds up. “But it is based roughly on actual science.” “I did have Kip Thorne read the script and he helped me out with some of the concepts, though we’re not going to make any case for this being scientifically accurate,” Nolan said in the film’s press notes. Nolan consulted Nobel laureate Kip Thorne on Tenet - Thorne also worked with the filmmaker on Interstellar - but ended up relegating some of his suggestions. Not to get into a physics lesson, but inversion is this idea of material that has had its entropy inverted, so it’s running backwards through time, relative to us.” It deals with time and the different ways in which time can function. Nolan in an earlier interview to Entertainment Weekly had said, “This film is not a time-travel film. The film introduces a concept known as ‘reverse entropy’. Starring John David Washington, Robert Pattinson, Elizabeth Debicki, Dimple Kapadia, Michael Caine, Kenneth Branagh and others, Tenet is Nolan’s take on an old-school spy thriller, with his own science-fiction spin. Criticism for the film seems to be focussed on two things: its complicated plot and unclear sound mixing. As a by-product, we also give a continuous analogue of some Plünnecke-Ruzsa inequalities from additive combinatorics.Christopher Nolan’s Tenet finally landed in India this Friday, and while fans of the filmmaker have been waiting with bated breath to finally be able to experience his vision on the big screen, the response hasn’t been unanimously positive. The proof relies on a demonstration of new relationships between the entropy of high dimensional random vectors and the volume of convex bodies, and on a study of effective supports of convex measures, both of which are of independent interest, as well as on Milman's deep technology of M-ellipsoids and on certain information-theoretic inequalities. The specialization of this inequality to log-concave measures may be seen as a version of Milman's reverse Brunn-Minkowski inequality. As a by-product, we also give a continuous analogue of some Plünnecke-Ruzsa inequalities from additive combinatorics.ĪB - We develop a reverse entropy power inequality for convex measures, which may be seen as an affine-geometric inverse of the entropy power inequality of Shannon and Stam. N2 - We develop a reverse entropy power inequality for convex measures, which may be seen as an affine-geometric inverse of the entropy power inequality of Shannon and Stam. National Science Foundation CAREER grant DMS-1056996. was supported by a Junior Faculty Fellowship from Yale University and the U.S. National Science Foundation grant DMS-1106530, and M.M. T1 - Reverse Brunn-Minkowski and reverse entropy power inequalities for convex measures
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