Past

The classical fractional crystallisation scenario for magma ocean solidification on the Moon suggests that its crust formed by flotation of light anorthite minerals on top of a liquid ocean, which has been used to explain the anorthositic composition of the lunar crust. However, this model points to rapid crustal formation over tens of million years and struggles to predict the age range of primitive ferroan anorthosites from 4.5 and 4.3 Ga. 

Here I will present a new paradigm of slushy magma ocean crystallisation in which crystals are suspended throughout the magma ocean, and the lunar crust forms by magmatic processes over several hundreds of thousand years.

We will then focus on the effects of the particular characteristics of this primary crust on the transport and eruption of magma on the Moon.

 “In this talk, I shall present the past research track passing through quantum mechanical studies of small molecules to biomolecules, to proteome-wide big data analyses and computational genomics. Next, the ongoing research in our group will be presented that builds upon the expertise on different levels of information processing in life (genome, transcriptome, proteins, small molecules), to develop self-consistent “first principles” models in biology with a wide spectrum of usage. The immediate benefits and the targeted processes will be described covering different layers of the central dogma of biology, multigenic diseases and disease driver/passenger mutation predictions."

We present a partial differential equation framework for deep residual neural networks and for the associated learning problem. This is done by carrying out the continuum limits of neural networks with respect to width and depth. We study the wellposedness, the large time solution behavior, and the characterization of the steady states of the forward problem. Several useful time-uniform estimates and stability/instability conditions are presented. We state and prove optimality conditions for the inverse deep learning problem, using standard variational calculus, the Hamilton-Jacobi-Bellmann equation and the Pontryagin maximum principle. This serves to establish a mathematical foundation for investigating the algorithmic and theoretical connections between neural networks, PDE theory, variational analysis, optimal control, and deep learning.

This is based on joint work with Hailiang Liu.

Amy Kent

Multiscale Mathematical Models for Tendon Tissue Engineering

Tendon tissue engineering aims to grow functional tendon in vitro. In bioreactor chambers, cells growing on a solid scaffold are fed with nutrient-rich media and stimulated by mechanical loads. The Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences is developing a Humanoid Robotic Bioreactor, where cells grow on a flexible fibrous scaffold actuated by a robotic shoulder. Tendon cells modulate their behaviour in response to shear stresses - experimentally, it is desirable to design robotic loading regimes that mimic physiological loads. The shear stresses are generated by flowing cell media; this flow induces deformation of the scaffold which in turn modulates the flow. Here, we capture this fluid-structure interaction using a homogenised model of fluid flow and scaffold deformation in a simplified bioreactor geometry. The homogenised model admits analytical solutions for a broad class of forces representing robotic loading. Given the solution to the microscale problem, we can determine microscale shear stresses at any point in the domain. In this presentation, we will outline the model derivation and discuss the experimental implications of model predictions.

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Michael Negus

High-Speed Droplet Impact Onto Deformable Substrates: Analysis And Simulations

The impact of a high-speed droplet onto a substrate is a highly non-linear, multiscale phenomenon and poses a formidable challenge to model. In addition, when the substrate is deformable, such as a spring-suspended plate or an elastic sheet, the fluid-structure interaction introduces an additional layer of complexity. We present two modeling approaches for droplet impact onto deformable substrates: matched asymptotics and direct numerical simulations. In the former, we use Wagner's theory of impact to derive analytical expressions which approximate the behaviour during the early stages of the impact. In the latter, we use the open source volume-of-fluid code Basilisk to conduct direct numerical simulations designed to both validate the analytical framework and provide insight into the later times of impact. Through both methods, we are able to observe how the properties of the substrate, such as elasticity, affect the behaviour of the flow. We conclude by showing how these methods are complementary, as a combination of both can lead to a thorough understanding of the droplet impact across timescales.

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Edwina Yeo

Modelling of Magnetically Targeted Stem Cell Delivery

Targeting delivery of stem cells to the site of an injury is a key challenge in regenerative medicine. One possible approach is to inject cells implanted withmagnetic nanoparticles into the blood stream. Cells can then be targeted to the delivery site by an external magnetic field. At the injury site, it is of criticalimportance that the cells do not form an aggregate which could significantly occlude the vessel.We develop a model for the transport of magnetically tagged cells in blood under the action of an external magnetic field. We consider a system of blood and stem cells in a single vessel.  We exploit the small aspect ratio of the vessel to examine the system asymptotically. We consider the system for a range of magnetic field strengths and varying strengths of the diffusion coefficient of the stem cells. We explore the different regimes of the model and determine the optimal conditions for the effective delivery of stem cells while minimising vessel occlusion.

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Helen Zha

Mathematical model of a valve-controlled, gravity driven bioreactor for platelet production

Hospitals sometimes experience shortages of donor blood platelet supplies, motivating research into~\textit{in vitro}~production of platelets. We model a novel platelet bioreactor described in Shepherd et al [1]. The bioreactor consists of an upper channel, a lower channel, and a cell-seeded porous collagen scaffold situated between the two. Flow is driven by gravity, and controlled by valves on the four inlets and outlets. The bioreactor is long relative to its width, a feature which we exploit to derive a lubrication reduction of unsteady Stokes flow coupled to Darcy. As the shear stress experienced by cells influences platelet production, we use our model to quantify the effect of varying pressure head and valve dynamics on shear stress.

[1] Shepherd, J.H., Howard, D., Waller, A.K., Foster, H.R., Mueller, A., Moreau, T., Evans, A.L., Arumugam, M., Chalon, G.B., Vriend, E. and Davidenko, N., 2018. Structurally graduated collagen scaffolds applied to the ex vivo generation of platelets from human pluripotent stem cell-derived megakaryocytes: enhancing production and purity. Biomaterials.

Agent-based models are an intuitive, interpretable way to model markets and give us a powerful mechanism to analyze counterfactual scenarios that might rarely occur in historical market data. However, building realistic agent-based models is challenging and requires that we (a) ensure that agent behaviors are realistic, and (b) calibrate the agent composition using real data. In this talk, we will present our work to build realistic agent-based models using a multi-agent reinforcement learning approach. Firstly, we show that we can learn a range of realistic behaviors for heterogeneous agents using a shared policy conditioned on agent parameters and analyze the game-theoretic implications of this approach. Secondly, we propose a new calibration algorithm (CALSHEQ) which can estimate the agent composition for which calibration targets are approximately matched, while simultaneously learning the shared policy for the agents. Our contributions make the building of realistic agent-based models more efficient and scalable.

Scientific machine learning is a burgeoning discipline for mixing machine learning into scientific simulation. Use cases of this field include automated discovery of physical equations and accelerating physical simulators. However, making the analyses of this field automated will require building a set of tools that handle stiff and ill-conditioned models without requiring user tuning. The purpose of this talk is to demonstrate how the methods and tools of scientific machine learning can be consolidated to give a single high performance and robust software stack. We will start by describing universal differential equations, a flexible mathematical object which is able to represent methodologies for equation discovery, 100-dimensional differential equation solvers, and discretizations of physics-informed neural networks. Then we will showcase how adjoint sensitivity analysis on the universal differential equation solving process gives rise to efficient and stiffly robust training methodologies for a large variety of scientific machine learning problems. With this understanding of differentiable programming we will describe how the Julia SciML Software Organization is utilizing this foundation to provide high performance tools for deploying battery powered airplanes, improving the energy efficiency of buildings, allow for navigation via the Earth's magnetic field, and more.

A link for this talk will be sent to our mailing list a day or two in advance.  If you are not on the list and wish to be sent a link, please send email to @email.

We analyze the steady motion of a viscous incompressible fluid in a two- and three-dimensional channel containing an obstacle through the Navier-Stokes equations under different types of boundary conditions. In the 2D case we take constant non-homogeneous Dirichlet boundary data in a (virtual) square containing the obstacle, and emphasize the connection between the appearance of lift and the unique solvability of Navier-Stokes equations. In the 3D case we consider mixed boundary conditions: the inflow is given by a fairly general datum and the flow is assumed to satisfy a constant traction boundary condition on the outlet. In the absence of external forcing, explicit bounds on the inflow velocity guaranteeing existence and uniqueness of such steady motion are provided after estimating some Sobolev embedding constants and constructing a suitable solenoidal extension of the inlet velocity. In the 3D case, this solenoidal extension is built through the Bogovskii operator and explicit bounds on its Dirichlet norm (in terms of the geometric parameters of the obstacle) are found by solving a variational problem involving the infinity-Laplacian.

The talk accounts for results obtained in collaboration with Filippo Gazzola and Ilaria Fragalà (both at Politecnico di Milano).

I will introduce left-orderable groups and discuss constructions and examples of such groups. I will then motivate studying left-orders under the framework of formal languages and discuss some recent results.

The Burali-Forti paradox suggests that the transfinite cardinals “go on forever,” surpassing any conceivable bound one might try to place on them. The traditional Zermelo-Frankel axioms for set theory fall into a hierarchy of axiomatic systems formulated by reasserting this intuition in increasingly elaborate ways: the large cardinal hierarchy. Or so the story goes. A serious problem for this already naive account of large cardinal set theory is the Kunen inconsistency theorem, which seems to impose an upper bound on the extent of the large cardinal hierarchy itself. If one drops the Axiom of Choice, Kunen’s proof breaks down and a new hierarchy of choiceless large cardinal axioms emerges. These axioms, if consistent, represent a challenge for those “maximalist” foundational stances that take for granted both large cardinal axioms and the Axiom of Choice. This talk concerns some recent advances in our understanding of the weakest of the choiceless large cardinal axioms and the prospect, as yet unrealized, of establishing their consistency and reconciling them with the Axiom of Choice.

A spanning tree of $G$ is a subgraph of $G$ with the same vertex set as $G$ that is a tree. In 1981, McKay proved an asymptotic result regarding the number of spanning trees in random $k$-regular graphs, showing that the number of spanning trees $\kappa_1(G_n)$ in a random $k$-regular graph on $n$ vertices satisfies $\lim_{n \to \infty} (\kappa_1(G_n))^{1/n} = c_{1,k}$ in probability, where $c_{1,k} = (k-1)^{k-1} (k^2-2k)^{-(k-2)/2}$.

In this talk we will discuss a high-dimensional of the matching model for simplicial complexes, known as random Steiner complexes. In particular, we will prove a high-dimensional counterpart of McKay's result and discuss the local limit of such random complexes. 
Based on a joint work with Lior Tenenbaum. 

For 20 years we have known that average values of characteristic polynomials of random unitary matrices provide a good model for moments of the Riemann zeta function.  Now we consider moments of the logarithmic derivative of characteristic polynomials, calculations which are motivated by questions on the distribution of zeros of the derivative of the Riemann zeta function.  Joint work with Emilia Alvarez. 

A link for this talk will be sent to our mailing list a day or two in advance.  If you are not on the list and wish to be sent a link, please send email to @email.

Let W be the Witt algebra of vector fields on the punctured complex plane, and let Vir be the Virasoro algebra, the unique nontrivial central extension of W.  We discuss work in progress with Alexey Petukhov to analyse Poisson ideals of the symmetric algebra of Vir.

We focus on understanding maximal Poisson ideals, which can be given as the Poisson cores of maximal ideals of Sym(Vir) and of Sym(W).  We give a complete classification of maximal ideals of Sym(W) which have nontrivial Poisson cores.  We then lift this classification to Sym(Vir), and use it to show that if $\lambda \neq 0$, then $(z-\lambda)$ is a maximal Poisson ideal of Sym(Vir).

Randomized experiments, or "A/B" tests, remain the gold standard for evaluating the causal effect of a policy intervention or product change. However, experimental settings such as social networks, where users are interacting and influencing one another, violate conventional assumptions of no interference needed for credible causal inference. Existing solutions include accounting for the fraction or count of treated neighbors in a user's network, among other strategies. Yet, there are often a high number of researcher degrees of freedom in specifying network interference conditions and most current methods do not account for the local network structure beyond simply counting the number of neighbors. Capturing local network structures is important because it can account for theories, such as structural diversity and echo chambers. Our study provides an approach that accounts for both the local structure in a user's social network via motifs as well as the assignment conditions of neighbors. We propose a two-part approach. We first introduce and employ "causal network motifs," i.e. network motifs that characterize the assignment conditions in local ego networks; and then we propose a tree-based algorithm for identifying different network interference conditions and estimating their average potential outcomes. We test our method on a real-world experiment on a large-scale network and a synthetic network setting, which highlight how accounting for local structures can better account for different interference patterns in networks.

Preconditioning techniques are widely used for speeding up the iterative solution of systems of linear equations, often by transforming the system into one with lower condition number. Even though the condition number also serves as the determining constant in simple bounds for the numerical error of the solution, simple experiments and bounds show that such preconditioning on the matrix level is not guaranteed to reduce this error. Transformations on the operator level, on the other hand, improve both accuracy and speed of iterative methods as predicted by the change of the condition number. We propose to investigate such methods under a common framework, which we call full operator preconditioning, and show practical examples.

A link for this talk will be sent to our mailing list a day or two in advance.  If you are not on the list and wish to be sent a link, please send an email to @email.

We study the minimum total weight of a disk triangulation using any number of vertices out of $\{1,..,n\}$ where the boundary is fixed and the $n \choose 3$ triangles have independent rate-1 exponential weights. We show that, with high probability, the minimum weight is equal to $(c+o(1))n-1/2\log n$ for an explicit constant $c$. Further, we prove that, with high probability, the minimum weights of a homological filling and a homotopical filling of the cycle $(123)$ are both attained by the minimum weight disk triangulation. We will discuss a related open problem concerning simple-connectivity of random simplicial complexes, where a similar phenomenon is conjectured to hold. Based on joint works with Itai Benjamini, Eyal Lubetzky, and Zur Luria.

What do random spanning trees, graph embeddings, random walks, simplices and graph curvature have in common? As you may have guessed from the title, they are indeed all intimately connected to the effective resistance on graphs! While originally invented as a tool to study electrical circuits, the effective resistance has proven time and again to be a graph characteristic with a variety of interesting and often surprising properties. Starting from a number of equivalent but complementary definitions of the effective resistance, we will take a stroll through some classical theorems (Rayleigh monotonicity, Foster's theorem), a few modern results (Klein's metricity, Fiedler's graph-simplex correspondence) and finally discuss number of recent developments (variance on graphs, discrete curvature and graph embeddings).

Motivated by an attempt to construct a theory of quantum gravity as a perturbation around some flat background, Penrose has shown that, despite being asymptotically flat, there is an inconsistency between the causal structure at infinity of Schwarzschild and Minkowski spacetimes. This suggests that such a perturbative approach cannot possibly work. However, the proof of this inconsistency is specific to 4 spacetime dimensions. In this talk I will discuss how this result extends to higher (and lower) dimensions. More generally, I will consider examples of how the causal structure of asymptotically flat spacetimes are affected by dimension and by the presence of mass (both positive and negative). I will then show how these ideas can be used to prove a higher dimensional extension of the positive mass theorem of Penrose, Sorkin and Woolgar.

Stochastic quantisation is, broadly speaking, the use of a stochastic differential equation to construct a given probability distribution. Usually this refers to Markovian Langevin evolution with given invariant measure. However we will show that it is possible to construct other kind of equations (elliptic stochastic partial differential equations) whose solutions have prescribed marginals. This connection was discovered in the '80 by Parisi and Sourlas in the context of dimensional reduction of statistical field theories in random external fields. This purely probabilistic results has a proof which depends on a supersymmetric formulation of the problem, i.e. a formulation involving a non-commutative random field defined on a non-commutative space. This talk is based on joint work with S. Albeverio and F. C. de Vecchi.

Sieve methods are analytic tools that we can use to tackle problems in additive number theory. This talk will serve as a gentle introduction to the area. At the end we will discuss recent progress on a variation on the prime $k$-tuples conjecture which involves sums of two squares. No knowledge of sieves is required!

We propose a modulated free energy which combines of the method previously developed by the speaker together with the modulated energy introduced by S. Serfaty. This modulated free energy may be understood as introducing appropriate weights in the relative entropy  to cancel the more singular terms involving the divergence of the flow. This modulated free energy allows to treat singular interactions of gradient-flow type and allows potentials with large smooth part, small attractive singular part and large repulsive singular part. As an example, a full rigorous derivation (with quantitative estimates) of some chemotaxis models, such as Patlak-Keller-Segel system in the subcritical regimes, is obtained.