Dynamics of leptons such as electrons and neutrinos play an important role in the evolution of core-collapse supernovae (CCSN). Nevertheless, chirality as one of fundamental microscopic properties that could affect lepton transport, through e.g. weak interaction, has been mostly overlooked. In this talk, I will discuss how chiral effects such as the renowned chiral magnetic effect (CME), generating an electric charge current along magnetic fields with chirality imbalance, could result in the unstable modes of magnetic fields and inverse cascade, which potentially influence the matter evolution in CCSN and pulsar kicks. I will also show how an effective CME could be realized via the backreaction from neutrino radiation even in the absence of an axial charge characterizing an unequal number of right- and left-handed electrons.

People's incentives during an infectious disease outbreak influence their behaviour, and this behaviour can impact how the outbreak unfolds. Early on during an outbreak, people are at little personal risk of infection and hence may be unwilling to change their lifestyle to slow the spread of disease. As the number of cases grows, however, people may then voluntarily take extreme measures to limit their exposure. Political leaders also respond to the welfare and changing desires of their constituents, through public health policies that themselves shape the course of the epidemic and its ultimate health and economic repercussions. In this talk I will use ideas from the study of differential games to model how individuals’ and politicians’ incentives change during an outbreak, and the epidemiological and economic consequences that ensue when these incentives are acted upon. Motivated by the COVID-19 pandemic, I focus on physical distancing behaviour and the imposition of stay-at-home orders by politicians. I show that there is a fundamental difference in the political, economic, and health consequences of an infectious disease outbreak depending on the degree of asymptomatic transmission. If transmission occurs primarily by asymptomatic carriers, then politicians will be incentivized to impose stay-at-home orders earlier and for longer than individuals would like. Despite such orders being unpopular, however, they ultimately benefit all individuals. On the other hand, if the disease is transmitted primarily by symptomatic infections, then individuals are incentivized to stay at home earlier and for longer than politicians would like. In this case, politicians will be incentivized to impose back-to-work orders that, despite being unpopular, will again ultimately be to the benefit of all individuals.

This is joint work with David McAdams, Fuqua School of Business and Economics Department, Duke University.

Lattice gauge theory is a powerful computational approach in quantum field theory. It is also utilizable for investigating quantum phenomena in curved spacetimes, such as rotating frame, torsion, and gravitational backgrounds. In this talk, I would like to overview the formulation and results of lattice simulations in curved spacetimes.

The formation of macromolecule patterns depending on chemical concentration under non-equilibrium conditions, first observed during morphogenesis, has recently been observed at the intracellular level, and its relevance as intracellular morphogen has been demonstrated in the case of bacterial cell division. These studies have discussed how cargos maintain positional information provided by chemical gradients. However, how cargo transports are directly mediated by chemical gradients remains unknown. Based on the previously proposed mechanism of chemotaxis-like behavior of cargos (referred to as chemophoresis), we introduce the chemophoresis engine as a physicochemical mechanism of cargo motion, which transforms chemical free energy to directed motion through the catalytic ATP hydrolysis [1]. We propose its possible role as a universal principle of hydrolysis-driven intracellular transport.

Entropy production is central to understanding nonequilibrium phenomena. It is known that decomposing entropy production enables us to separately treat distinct two aspects of dynamics, nonstationarity and breaking of detailed balance. In this seminar, I talk about our recent progress on geometric decomposition of entropy production in discrete stochastic systems and deterministic chemical systems. For the audience who may not be familiar with nonequilibrium thermodynamics and linear algebraic graph theory, which the latter enables us to treat the two kinds of systems at once, I would like to start with a very basic introduction. Then I explain why and how we decompose entropy production. Specifically, I mainly focus on the "Onsager-projective decomposition" we study in arXiv:2205.15227 rather than the information geometric decomposition provided in the following paper arXiv:2206.14599. Further, several physical consequences will be discussed, including generalization of Schnakenberg's decomposition stemming from cycles in a steady system, and its relation to gradient flow expressions of a master equation and a rate equation.

Binary neutron star (NS) merger is a promising site for the rapid neutron capture nucleosynthesis (r-process). The radioactive decay of newly synthesized elements powers electromagnetic radiation, as called kilonova. The detection of gravitational wave from a NS merger GW170817 and the observation of the associated kilonova AT2017gfo have provided with us the evidence that r-process happens in the NS merger. However, the abundance pattern synthesized in this event, which is important to understand the origin of the r-process elements, is not yet clear. In this talk, I will first introduce an overview and current understanding of kilonova. Then, I will discuss our recent findings of elemental features in photospheric spectra of kilonova toward identification of elements.

The movement and deformation of the Earth’s crust and upper mantle provide critical insights into the evolution of earthquake processes and future earthquake potentials. Crustal deformation can be modeled by dislocation models that represent earthquake faults in the crust as defects in a continuum medium. In this study, we propose a physics-informed deep learning approach to model crustal deformation due to earthquakes. Neural networks can represent continuous displacement fields in arbitrary geometrical structures and mechanical properties of rocks by incorporating governing equations and boundary conditions into a loss function. The polar coordinate system is introduced to accurately model the displacement discontinuity on a fault as a boundary condition. We illustrate the validity and usefulness of this approach through example problems with strike-slip faults. This approach has a potential advantage over conventional approaches in that it could be straightforwardly extended to high dimensional, anelastic, nonlinear, and inverse problems.

iTHEMS-AIP Joint Colloquium

Optimal transport (OT) has recently gained a lot of interest in machine learning. It is a natural tool to compare in a geometrically faithful way probability distributions. It finds applications in both supervised learning (using geometric loss functions) and unsupervised learning (to perform generative model fitting). OT is however plagued by the curse of dimensionality, since it might require a number of samples which grows exponentially with the dimension. In this talk, I will explain how to leverage entropic regularization methods to define computationally efficient loss functions, approximating OT with a better sample complexity. More information and references can be found on the website of our book "Computational Optimal Transport" (see related link below).