Maths on the Move plus.maths.org

Mathematics is a creative pursuit so it's not surprising that there are communalities between maths and art in all its forms. In this episode we explore the intersection between maths and art with physicist Andrzej Herczyński and mathematician Paul Glendinning.
Andrzej Herczyński

Andrzej and Paul were two of the organisers of the workshop Space, scale and scaling in art, which recently took place at the Isaac Newton Institute for Mathematical Sciences in Cambridge. We find out about the rich dialogue that can ensue between artists and mathematicians, how maths and physics can help us understand the power of art and how we perceive it, and provide insights into how a particular piece of art was made.
The Space, scale and scaling in art workshop was part of a larger research programme funded by the National Science Foundation on the intersection of science and art.
You can see Agnes Martin's painting Morning, which is mentioned in this episode, here (though the digital version does not do it justice). To see Jackson Pollock's works, which are also discussed in this episode, go to the Jackson Pollock website. The image above has been generated by AI.
Paul Glendinning

To find out more about some of the topics mentioned in this episode, see the following articles:
Fractal expressionism looks at fractal structures in works by the abstract expressionist Jackson Pollock.
The Artist's fractal fingerprint explores Pollock's paintings further.
Sine language looks at a song by our musician friend Oli Freke which explores the idea of sine waves, and how they relate to other concepts such as the Western tuning system known as equal temperament and even to ancient Greek cosmological ideas.
Fractal music has composer Dmitry Kormann explaining how he brings fractal-like patterns to the very structure of his music, with beautiful results.
Restoring profanity explores how the heat equation can help restore damaged art works.
Where to stand to look at sculptures uses some simple geometry to find the perfect vantage point from which to take in a sculpture (or painting).
To see all our content on maths and art see here, and for everything on maths and music see here.
This content was produced as part of our collaboration with the Isaac Newton Institute for Mathematical Sciences (INI) – you can find all the content from our collaboration here. The INI is an international research centre and our neighbour here on the University of Cambridge's maths campus. It attracts leading mathematical scientists from all over the world, and is open to all. Visit www.newton.ac.uk to find out more.

One of the most fascinating figures in the history of mathematics was Srinivasa Ramanujan, a self-taught Indian genius who formed a remarkable relationship with the Cambridge mathematician GH Hardy. Ramanujan was interested in problems in number theory, which are often easy to state, but incredibly difficult to prove. One amazing thing about Ramanujan's work is that it still finds applications today, in areas you'd never imagine are linked to number theory. An  example is the study of black holes, those gravitational monstrosities that lie at the centres of galaxies.
We will explore this surprising link in an upcoming episode, but for now we revisit a 2018 interview with mathematician Ken Ono (pictured above), who was an advisor and associate producer on the well-known film about Ramanujan, The man who knew infinity. Talking to Plus Editor Rachel Thomas, Ken explores just what made Ramanujan's work so special and the piece of mathematics that is relevant to black holes. Rachel talked to Ken at the Royal Society's celebration of the centenary of Ramanujan's election as a Fellow of the Royal Society.
You can also read an article accompanying this podcast, which looks at the mathematics relevant to black holes. For more about Ramanujan's mathematics, and Ken's research into it, see Ramanujan surprises again. To find out more about the Spirit of Ramanujan project, which Ken mentions in this episode, see here.

In this episode of Maths on the move we look at some favourite pieces of maths we have worked on so far this year. From a revolutionary new tile to new insights in topology, and from fooling cancer cells to bringing mathematical research into the classroom, we hope there's something interesting there for everyone. 
To find out more about the topics mentioned in this episode see the following articles:
A tip of the hat: Celebrating an aperiodic monotile — meeting the discoverers of the hat
Contagious maths — bringing epidemiological research into the classroom
The mathematics of movement — what do cancer cells, birds, and whales have in common (and can a slime mould be intelligent)?
Outraged by not knowing— new insights in topology with Oscar Randal-Williams
To find out more about our work with the JUNIPER network of disease modellers see here and to find out more about our work with the maths4DL research project see here.

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It's always exciting to have a glimpse at new mathematics and technology as they take shape.  In this podcast we talk to Georg Maierhofer, from the University of Oxford, about an exciting new idea that is only just emerging  – physics informed neural networks  (PINNs for short) – where you add in the laws of physics to machine learning methods. 
We have been able to sit in on a number of meetings of our colleagues from Maths4DL (the Mathematics for Deep Learning research group) as they explore this idea.   Georg explains why PINNs are a bit like learning golf, tells us about the exciting opportunities and challenges, and why the key part to developing new ideas is getting the right people together at the right time.

You can find more about the machine learning and the some of the work that Maths4DL is doing at https://plus.maths.org/maths4dl, including our recent podcast How does AI work?  and our collection Predicting the weather with artificial intelligence.
This content is part of our collaboration with the Mathematics for Deep Learning (Maths4DL) research programme, which brings together researchers from the universities of Bath and Cambridge, and University College London. Maths4DL aims to combine theory, modelling, data and computation to unlock the next generation of deep learning. You can see more content produced with Maths4DL here.

Last weekend our friends and neighbours at the Centre for Mathematical Sciences at the University of Cambridge put on a great event: the Mathematics Discovery Day, part of the Cambridge Festival. Among the may hands-on activities, games and pop-up explorations were the hugely popular, and well-attended, workshops for students delivered by our colleagues Liz and Charlie from NRICH. Our brilliant colleague Julia Hawkins herded academics and volunteers, juggled props and generally made sure that everything went smoothly.
At the same time our partners at the Isaac Newton Institute next door hosted one of our favourite physicists: Ben Allanach, Professor of Theoretical Physics at the University of Cambridge. Ben gave a talk called The force awakens: Quantum collisions, in which he explored experiments at the Large Hadron Collider (LHC), particle physics, as well as recent research results which suggested there may be a fifth force of nature, hitherto unknown to science.
For those who weren't able to attend Ben's talk we revisit an interview with him from last year, in which he explains this intriguing (and if true sensational) result about a potential new force.
The image above illustrates particle collisions at the LHC and is courtesy CMS.
This content now forms part of our collaboration with the Isaac Newton Institute for Mathematical Sciences (INI). The INI is an international research centre in Cambridge which attracts leading mathematicians from all over the world. You can find all the content from the collaboration here.

Artificial intelligence has made astonishing progress in the last few years. Perhaps surprisingly, all of the amazing things we've seen, from ChatGPT to generative AI, are powered by same mathematical technique: machine learning, and in particular deep learning.
In this episode of Maths on the move we talk to Kweku Abraham, member of Maths4DL, a research project which investigates deep learning, and postdoctoral researcher at the University of Cambridge. Kweku explains how machine learning works, why it's so powerful and whether there are any limits to what it can achieve, and the kind of maths he works on every day.
To find out more about the topics discussed in this episode, see Artificial intelligence and deep learning: Your questions answered.
This content is part of our collaboration with the Mathematics for Deep Learning (Maths4DL) research programme, which brings together researchers from the universities of Bath and Cambridge, and University College London. Maths4DL aims to combine theory, modelling, data and computation to unlock the next generation of deep learning. You can see more content produced with Maths4DL here.