skip to content

IMAGES

a network for developers and users of imaging and analysis tools
 
Subscribe to Talks feed
A personal list of talks.
Updated: 29 min 18 sec ago

Thu 07 Mar 15:00: Hamiltonian simulation and optimal control

Tue, 27/02/2024 - 09:51
Hamiltonian simulation and optimal control

Hamiltonian simulation on quantum computers is one of the primary candidates for demonstration of quantum advantage. A central tool in Hamiltonian simulation is the matrix exponential. While uniform polynomial approximations (Chebyshev), best polynomial approximations, and unitary but asymptotic rational approximations (Padé) are well known and are extensively used in computational quantum mechanics, there was an important gap which has now been filled by the development of the theory and algorithms for unitary rational best approximations. This class of approximants leads to geometric numerical integrators with excellent approximation properties. In the second part of the talk I will talk about time-dependent Hamiltonians for many-body two-level systems, including a quantum algorithm for their simulation and some (classical) optimal control algorithms for quantum gate design.

Add to your calendar or Include in your list

Thu 23 May 15:00: TBA

Thu, 22/02/2024 - 15:06
TBA

Abstract not available

Add to your calendar or Include in your list

Fri 22 Mar 09:00: SCIENCE AND THE FUTURES OF MEDICINE One Day Meeting Check website for latest updates and booking information http://www.cambridgephilosophicalsociety.org

Thu, 22/02/2024 - 10:42
SCIENCE AND THE FUTURES OF MEDICINE One Day Meeting

Recent advances in the sciences underpinning medicine, and their translation to clinical impact, are transforming our ability to understand and treat human diseases. This one-day meeting will explore emerging areas in which the convergence of fundamental science and translational opportunities promises to shape the futures of medicine.

Programme

09.00-09.15 Introduction to meeting

09.15-10.15 Serena Nik-Zainal, Professor of Genomic Medicine and Bioinformatics, Department of Medical Genetics, School of Clinical Medicine, University of Cambridge – Genomes, genome engineering and personalised medicine

10.15-11.15 Shyni Varghese, Professor of Biomedical Engineering, Mechanical Engineering and Materials Science and Orthopaedics, Duke University, US - Tissue engineering

11.15-11.45 Morning Coffee

11.45- 12.45 Jan Hoeijmakers, Department of Molecular Genetics, Erasmus University, Rotterdam and Cologne, Princess Maxima Center for Pediatric Oncology, Oncode, Utrecht, both in the Netherlands and the CECAD , Cologne, Germany – DNA damage, cancer and aging, the unexpected impact of nutrition on medicine

12.45-13.45 Lunch

13.45-14.45 Paul Workman, Professor of Pharmacology and Therapeutics, Centre for Cancer Drug Discovery, The Institute of Cancer Research, London – Transforming small molecule cancer drug discovery for precision medicine

14.45-15.45 Iain Buchan, W.H. Duncan Chair in Public Health Systems, Associate Pro Vice Chancellor for Innovation, Public Health, Policy & Systems, University of Liverpool – How might artificial intelligence augment population health?

15.45-16.15 Afternoon Tea

16.15- 17.15 Alessio Ciulli, School of Life Sciences, University of Dundee – New approaches in drug discovery

17.15 Closing remarks

Check website for latest updates and booking information http://www.cambridgephilosophicalsociety.org

Add to your calendar or Include in your list

Thu 29 Feb 15:00: Efficient frequency-dependent numerical simulation of wave scattering problems

Wed, 21/02/2024 - 14:26
Efficient frequency-dependent numerical simulation of wave scattering problems

Wave propagation in homogeneous media is often modelled using integral equation methods. The boundary element method (BEM) is for integral equations what the finite element method is for partial differential equations. One difference is that BEM typically leads to dense discretization matrices. A major focus in the field has been the development of fast solvers for linear systems involving such dense matrices. Developments include the fast multipole method (FMM) and more algebraic methods based on the so-called H-matrix format. Yet, for time-harmonic wave propagation, these methods solve the original problem only for a single frequency. In this talk we focus on the frequency-sweeping problem: we aim to solve the scattering problem for a range of frequencies. We exploit the wavenumber-dependence of the dense discretization matrix for the 3D Helmholtz equation and demonstrate a memory-compact representation of all integral operators involved which is valid for a continuous range of frequencies, yet comes with a cost of a only small number of single frequency simulations. This is joined work at KU Leuven with Simon Dirckx, Kobe Bruyninckx and Karl Meerbergen.

Add to your calendar or Include in your list

Mon 19 Feb 14:00: SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning

Mon, 19/02/2024 - 21:12
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning

Deep Reinforcement Learning (DRL) has shown significant promise for uncovering sophisticated control policies that interact in environments with complicated dynamics, such as stabilizing the magnetohydrodynamics of a tokamak reactor and minimizing the drag force exerted on an object in a fluid flow. However, these algorithms require many training examples and can become prohibitively expensive for many applications. In addition, the reliance on deep neural networks results in an uninterpretable, black-box policy that may be too computationally challenging to use with certain embedded systems. Recent advances in sparse dictionary learning, such as the Sparse Identification of Nonlinear Dynamics (SINDy), have shown to be a promising method for creating efficient and interpretable data-driven models in the low-data regime. In this work, we extend ideas from the SIN Dy literature to introduce a unifying framework for combining sparse dictionary learning and DRL to create efficient, interpretable, and trustworthy representations of the dynamics model, reward function, and control policy. We demonstrate the effectiveness of our approaches on benchmark control environments and challenging fluids problems, achieving comparable performance to state-of-the-art DRL algorithms using significantly fewer interactions in the environment and an interpretable control policy orders of magnitude smaller than a deep neural network policy.

Add to your calendar or Include in your list

Wed 14 Feb 14:00: SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning

Wed, 14/02/2024 - 13:35
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning

Deep Reinforcement Learning (DRL) has shown significant promise for uncovering sophisticated control policies that interact in environments with complicated dynamics, such as stabilizing the magnetohydrodynamics of a tokamak reactor and minimizing the drag force exerted on an object in a fluid flow. However, these algorithms require many training examples and can become prohibitively expensive for many applications. In addition, the reliance on deep neural networks results in an uninterpretable, black-box policy that may be too computationally challenging to use with certain embedded systems. Recent advances in sparse dictionary learning, such as the Sparse Identification of Nonlinear Dynamics (SINDy), have shown to be a promising method for creating efficient and interpretable data-driven models in the low-data regime. In this work, we extend ideas from the SIN Dy literature to introduce a unifying framework for combining sparse dictionary learning and DRL to create efficient, interpretable, and trustworthy representations of the dynamics model, reward function, and control policy. We demonstrate the effectiveness of our approaches on benchmark control environments and challenging fluids problems, achieving comparable performance to state-of-the-art DRL algorithms using significantly fewer interactions in the environment and an interpretable control policy orders of magnitude smaller than a deep neural network policy.

Add to your calendar or Include in your list

Thu 22 Feb 15:00: Computing lower eigenvalues on rough domains

Tue, 06/02/2024 - 22:54
Computing lower eigenvalues on rough domains

In this talk I will describe a strategy for finding sharp upper and lower numerical bounds of the Poincare constant on a class of planar domains with piecewise self-similar boundary. The approach is developed in [A] and it consists of four main blocks: 1) tight inner-outer shape interpolation, 2) conformal mapping of the approximate polygonal regions, 3) grad-div system formulation of the spectral problem and 4) computation of the eigenvalue bounds. After describing the method, justifying its validity and reporting on general convergence estimates, I will show concrete evidence of its effectiveness on the Koch snowflake. I will conclude the talk by discussing potential applications to other linear operators on rough regions. This research has been conducted jointly with Lehel Banjai (Heriot-Watt University).

[A] J. Fractal Geometry 8 (2021) No. 2, pp. 153-188

Add to your calendar or Include in your list

Thu 15 Feb 15:00: Adaptive Intrusive Methods for Forward UQ in PDEs

Sun, 28/01/2024 - 17:54
Adaptive Intrusive Methods for Forward UQ in PDEs

In this talk we discuss a so-called intrusive approach for the forward propagation of uncertainty in PDEs with uncertain coefficients. Specifically, we focus on stochastic Galerkin finite element methods (SGFEMs). Multilevel variants of such methods provide polynomial-based surrogates with spatial coefficients that reside in potentially different finite element spaces. For elliptic PDEs with diffusion coefficients represented as affine functions of countably infinitely many parameters, well established theoretical results state that such methods can achieve rates of convergence independent of the number of input parameters, thereby breaking the curse of dimensionality. Moreover, for nice enough test problems, it is even possible to prove convergence rates afforded to the chosen finite element method for the associated deterministic PDE . However, achieving these rates in practice using automated computational algorithms remains highly challenging, and non-intrusive multilevel sampling methods are often preferred for their ease of use. We discuss an adaptive framework that is driven by a classical hierarchical a posteriori error estimation strategy — modified for the more challenging parametric PDE setting — and present numerical results.

Add to your calendar or Include in your list

Thu 01 Feb 15:00: What happens when you chop an equation?

Sat, 27/01/2024 - 11:05
What happens when you chop an equation?

This talk will discuss a tricky business: truncating a differential equation to produce finite solutions. A truncation scheme is often built directly into the steps needed to create a numerical system. E.g., finite differences replace exact differential operators with more manageable shadows, sweeping the exact approach off the stage.

In contrast, this talk will discuss the “tau method” which adds an explicit parameterised perturbation to an original equation. By design, the correction calls into existence an exact (finite polynomial) solution to the updated analytic system. The hope is that the correction comes out minuscule after comparing it with a hypothetical exact solution. The tau method has worked splendidly in practice, starting with Lanczos’s original 1938 paper outlining the philosophy. However, why the scheme works so well (and when it fails) remains comparably obscure. While addressing the theory behind the Tau method, this talk will answer at least one conceptual question: Where does an infinite amount of spectrum go when transitioning from a continuous differential equation to an exact finite matrix representation?

Add to your calendar or Include in your list

Thu 25 Jan 15:00: The future of governing equations

Thu, 25/01/2024 - 14:11
The future of governing equations

A major challenge in the study of dynamical systems is that of model discovery: turning data into reduced order models that are not just predictive, but provide insight into the nature of the underlying dynamical system that generated the data. We introduce a number of data-driven strategies for discovering nonlinear multiscale dynamical systems and their embeddings from data. We consider two canonical cases: (i) systems for which we have full measurements of the governing variables, and (ii) systems for which we have incomplete measurements. For systems with full state measurements, we show that the recent sparse identification of nonlinear dynamical systems (SINDy) method can discover governing equations with relatively little data and introduce a sampling method that allows SIN Dy to scale efficiently to problems with multiple time scales, noise and parametric dependencies. For systems with incomplete observations, we show that the Hankel alternative view of Koopman (HAVOK) method, based on time-delay embedding coordinates and the dynamic mode decomposition, can be used to obtain a linear models and Koopman invariant measurement systems that nearly perfectly captures the dynamics of nonlinear quasiperiodic systems. Neural networks are used in targeted ways to aid in the model reduction process. Together, these approaches provide a suite of mathematical strategies for reducing the data required to discover and model nonlinear multiscale systems.

Add to your calendar or Include in your list

Thu 25 Jan 15:00: The future of governing equations

Sun, 21/01/2024 - 12:28
The future of governing equations

A major challenge in the study of dynamical systems is that of model discovery: turning data into reduced order models that are not just predictive, but provide insight into the nature of the underlying dynamical system that generated the data. We introduce a number of data-driven strategies for discovering nonlinear multiscale dynamical systems and their embeddings from data. We consider two canonical cases: (i) systems for which we have full measurements of the governing variables, and (ii) systems for which we have incomplete measurements. For systems with full state measurements, we show that the recent sparse identification of nonlinear dynamical systems (SINDy) method can discover governing equations with relatively little data and introduce a sampling method that allows SIN Dy to scale efficiently to problems with multiple time scales, noise and parametric dependencies. For systems with incomplete observations, we show that the Hankel alternative view of Koopman (HAVOK) method, based on time-delay embedding coordinates and the dynamic mode decomposition, can be used to obtain a linear models and Koopman invariant measurement systems that nearly perfectly captures the dynamics of nonlinear quasiperiodic systems. Neural networks are used in targeted ways to aid in the model reduction process. Together, these approaches provide a suite of mathematical strategies for reducing the data required to discover and model nonlinear multiscale systems.

Add to your calendar or Include in your list

Mon 12 Feb 18:00: Going beyond emissions reduction – Climate Repair Check website for latest updates and booking information http://www.cambridgephilosophicalsociety.org

Thu, 18/01/2024 - 18:09
Going beyond emissions reduction – Climate Repair

The lecture will firstly summarise exactly where we are with climate change and crucially what the scientists are now considering in terms of the future. A future based purely on emissions reductions cannot keep the world below 1.5C.

We discuss some of the exciting ideas for greenhouse gas removal, and importantly going beyond terrestrial-based carbon dioxide removal. We will explore some of the approaches for marine carbon dioxide removal as well as the development of materials to accelerate the rate of oxidation of methane.

We will then spend time discussing what additional options we might have beyond emissions reduction and greenhouse gas removal; whilst these are necessary, even the most optimistic and ambitious scenarios considered by the IPCC indicate that they are not sufficient to keep temperatures below 1.5C. We will therefore review engineering concepts to limit temperature rise or interventions to protect glaciers and sea-ice, and ostensibly buy us time to stave off the worst effects of climate change whilst we get greenhouse gas levels down.

We will explore the different technologies which are being researched at the University of Cambridge in collaboration with multiple partner universities around the world, as well as the issues of public attitudes, governance and ethics associated with such research and potential deployment.

Check website for latest updates and booking information http://www.cambridgephilosophicalsociety.org

Add to your calendar or Include in your list

Mon 29 Jan 18:00: G I TAYLOR LECTURE - The influence of GI Taylor: granular collapses, viscous gravity currents, explosive eruptions and chemical gardens Check website for latest updates and booking information http://www.cambridgephilosophicalsociety.org

Thu, 18/01/2024 - 18:06
G I TAYLOR LECTURE - The influence of GI Taylor: granular collapses, viscous gravity currents, explosive eruptions and chemical gardens

The presentation will start will a short summary of the seminal work of G. I. Taylor and his most famous student, G. K. Batchelor. Evaluations of the propagation of muti-sized granular material under a variety of conditions will then be described, as well as being illustrated with desk top experiments.

The lecture will then discuss the all important flow of viscous gravity currents, again illustrated by desk top experiments and actual photos and explanations of the recent eruption of the Soufriere of St. Vincent. A description of the development of chemical gardens will then be described, initially experimented upon by Johan Glauber, said to be the first chemical engineer, and then by Isaac Newton. It is said by some that chemical gardens are the origin of life, at deep-sea smokers, as will be described.

Check website for latest updates and booking information http://www.cambridgephilosophicalsociety.org

Add to your calendar or Include in your list

Mon 26 Feb 18:00: The quest for the first stars and first black holes with the James Webb Space Telescope Check website for latest updates and booking information http://www.cambridgephilosophicalsociety.org

Thu, 18/01/2024 - 18:04
The quest for the first stars and first black holes with the James Webb Space Telescope

Finding and understanding the nature of the first stars at cosmic dawn is one of the most important and most ambitious goals for modern astrophysics. The first populations of stars produced the first chemical elements heavier than helium and formed the first, small protogalaxies, which then evolved, across the cosmic epoch, into the large and mature galaxies, such as the Milky Way and those in our local neighbour. Equally important and equally challenging is the search, in the early Universe, of the seeds of the first population of black holes, which later evolved in the supermassive black holes at the centre of galaxies, with masses even exceeding a billion times the mass of the Sun. When matter accretes on such supermassive black holes it can become so luminous to vastly outshine the light emitted by all stars in their host galaxy.

Since its launch, about two years ago, the James Webb Space Telescope has been revolutionizing this area of research. Its sensitivity in detecting infrared light from the remotest parts of the Universe is orders of magnitude higher than any previous observatory, an historical leap in astronomy and, more broadly, in science. I will presents some of the first, extraordinary discoveries from the Webb telescope, which have resulted in several unexpected findings. I will also discuss the new puzzles and areas of investigation that have been opened by Webb’s observations, how these challenge theoretical models, and the prospects of further progress in the coming years.

Check website for latest updates and booking information http://www.cambridgephilosophicalsociety.org

Add to your calendar or Include in your list

Thu 18 Jan 15:00: Computing the Spectra and Pseudospectra of Band-Dominated and Random Operators

Thu, 11/01/2024 - 09:08
Computing the Spectra and Pseudospectra of Band-Dominated and Random Operators

I will give an overview of my work over the last 15 years, with collaborators including Marko Lindner (TU Hamburg), Ratchanikorn Chonchaiya (King Mongkut’s University of Technology, Bangkok), Raffael Hagger (Kiel), and Brian Davies (KCL), on computing the spectra and pseudospectra of banded and band-dominated operators. This will include describing algorithms that, given appropriate inputs, can produce a convergent sequence of approximations to the spectrum of an arbitrary band-dominated operator, with the property that each member of the sequence can be computed in finitely many arithmetical operations. We give a concrete implementation of the algorithm for operators that are pseudoergodic in the sense of Davies (Commun. Math. Phys. 2001) and illustrate this algorithm by spectral computations for the beautiful Feinberg-Zee random hopping matrix. Details can be found at https://arxiv.org/abs/2401.03984

Add to your calendar or Include in your list

Thu 14 Mar 15:00: TBA

Mon, 08/01/2024 - 11:01
TBA

Abstract not available

Add to your calendar or Include in your list

Thu 07 Mar 15:00: TBA

Mon, 08/01/2024 - 10:58
TBA

Abstract not available

Add to your calendar or Include in your list

Thu 29 Feb 15:00: TBA

Mon, 08/01/2024 - 10:55
TBA

Abstract not available

Add to your calendar or Include in your list

Thu 22 Feb 15:00: TBA

Mon, 08/01/2024 - 10:53
TBA

Abstract not available

Add to your calendar or Include in your list

Thu 15 Feb 15:00: TBA

Mon, 08/01/2024 - 10:52
TBA

Abstract not available

Add to your calendar or Include in your list