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John J. Molina

Publications and source records attributed to John J. Molina.

17 recordsLinked to original sources

A deviatoric-stress closure for constitutive modeling of viscoelastic dynamics

Standard rheological measurements yield only selected stress components; thus, inferring tensorial constitutive equations from experimentally accessible observables is complicated. We propose a constitutive formulation written in terms of a deviatoric stress tensor, whose trace is zero, rather than the extra stress tensor. From rheometric data including shear stress, first and second normal stress differences under shear, and elongational stress under uniaxial elongation, we can construct a deviatoric stress state without the indeterminate isotropic stress. The deviatoric-stress dynamics is represented by a closure inferred through symbolic regression, constrained to satisfy material objectivity and a given linear Maxwell response. To demonstrate the proposed formulation, two closures inferred from stress responses of the Giesekus and Larson models successfully captured untrained transient-flow responses under planar elongation and mixed shear/uniaxial elongations at deformation rates around an inverse relaxation time. Steady rheological functions of the closures agreed with the original models in the linear-response regime and over a deformation-rate range connected to the training data, whereas deviations and divergent responses appeared at larger deformation rates outside the training regime. These results demonstrate that the proposed deviatoric-stress formulation provides a practical route for constitutive modeling of observable linear and nonlinear viscoelastic dynamics, while clarifying its range of validity under strong deformation.

cond-mat.soft↗

Propulsion of a chiral swimmer in viscoelastic fluids

Microswimmers often use chirality to generate translational movement from rotation motion, exhibiting distinct behaviors in complex fluids compared to simple Newtonian fluids. However, the underlying mechanism remains incompletely understood. In this study, we elucidate the precise mechanisms underlying the distinct behaviors of microswimmers in Newtonian and non-Newtonian fluids. We show that the enhanced speed of chiral swimmers is attributed to the Weissenberg effect induced by normal stress differences resulting from chiral flows. Additionally, we identify swimmer-specific normal stress differences in a viscoelastic fluid and demonstrate that swimming speed varies depending on whether the swimmer acts as a pusher or a puller. Moreover, we investigate the hydrodynamic interactions between a pair of chiral squirmers. When the squirmers are aligned parallel (perpendicular) to their swimming axis, they tend to separate (approach). These findings deepen our comprehension of the rheological properties of viscoelastic fluids containing microswimmers, promising advancements in various applications.

cond-mat.soft↗

Nash epidemics

Faced with a dangerous epidemic humans will spontaneously social distance to reduce their risk of infection at a socio-economic cost. Compartmentalised epidemic models have been extended to include this endogenous decision making: Individuals choose their behaviour to optimise a utility function, self-consistently giving rise to population behaviour. Here we study the properties of the resulting Nash equilibria, in which no member of the population can gain an advantage by unilaterally adopting different behaviour. We leverage a new analytic solution to obtain, (1) a simple relationship between rational social distancing behaviour and the current number of infections; (2) new scaling results for how the infection peak and number of total cases depend on the cost of contracting the disease; (3) characteristic infection costs that divide regimes of strong and weak behavioural response and depend only on the basic reproduction number of the disease; (4) a closed form expression for the value of the utility. We discuss how these analytic results provide a deep and intuitive understanding into the disease dynamics, useful for both individuals and policymakers. In particular the relationship between social distancing and infections represents a heuristic that could be communicated to the population to encourage, or "bootstrap", rational behaviour.

econ.TH↗

Rational social distancing policy during epidemics with limited healthcare capacity

Epidemics of infectious diseases posing a serious risk to human health have occurred throughout history. During recent epidemics there has been much debate about policy, including how and when to impose restrictions on behaviour. Policymakers must balance a complex spectrum of objectives, suggesting a need for quantitative tools. Whether health services might be `overwhelmed' has emerged as a key consideration. Here we show how costly interventions, such as taxes or subsidies on behaviour, can be used to exactly align individuals' decision making with government preferences even when these are not aligned. In order to achieve this, we develop a nested optimisation algorithm of both the government intervention strategy and the resulting equilibrium behaviour of individuals. We focus on a situation in which the capacity of the healthcare system to treat patients is limited and identify conditions under which the disease dynamics respect the capacity limit. We find an extremely sharp drop in peak infections at a critical maximum infection cost in the government's objective function. This is in marked contrast to the gradual reduction of infections if individuals make decisions without government intervention. We find optimal interventions vary less strongly in time when interventions are costly to the government and that the critical cost of the policy switch depends on how costly interventions are.

econ.TH↗

Rational social distancing in epidemics with uncertain vaccination timing

During epidemics people may reduce their social and economic activity to lower their risk of infection. Such social distancing strategies will depend on information about the course of the epidemic but also on when they expect the epidemic to end, for instance due to vaccination. Typically it is difficult to make optimal decisions, because the available information is incomplete and uncertain. Here, we show how optimal decision-making depends on information about vaccination timing in a differential game in which individual decision-making gives rise to Nash equilibria, and the arrival of the vaccine is described by a probability distribution. We predict stronger social distancing the earlier the vaccination is expected and also the more sharply peaked its probability distribution. In particular, equilibrium social distancing only meaningfully deviates from the no-vaccination equilibrium course if the vaccine is expected to arrive before the epidemic would have run its course. We demonstrate how the probability distribution of the vaccination time acts as a generalised form of discounting, with the special case of an exponential vaccination time distribution directly corresponding to regular exponential discounting.

econ.TH↗

Learning to swim efficiently in a nonuniform flow field

Microswimmers can acquire information on the surrounding fluid by sensing mechanical queues. They can then navigate in response to these signals. We analyse this navigation by combining deep reinforcement learning with direct numerical simulations to resolve the hydrodynamics. We study how local and non-local information can be used to train a swimmer to achieve particular swimming tasks in a non-uniform flow field, in particular a zig-zag shear flow. The swimming tasks are (1) learning how to swim in the vorticity direction, (2) the shear-gradient direction, and (3) the shear flow direction. We find that access to lab frame information on the swimmer's instantaneous orientation is all that is required in order to reach the optimal policy for (1,2). However, information on both the translational and rotational velocities seem to be required to achieve (3). Inspired by biological microorganisms we also consider the case where the swimmers sense local information, i.e. surface hydrodynamic forces, together with a signal direction. This might correspond to gravity or, for micro-organisms with light sensors, a light source. In this case, we show that the swimmer can reach a comparable level of performance as a swimmer with access to lab frame variables. We also analyse the role of different swimming modes, i.e. pusher, puller, and neutral swimmers.

physics.flu-dyn↗

Dynamics of microswimmers near a liquid-liquid interface with viscosity difference

Transport of material across liquid interfaces is ubiquitous for living cells and is also a crucial step in drug delivery and in many industrial processes. The fluids that are present on either side of the interfaces will usually have different viscosities. We present a physical model for the dynamics of microswimmers near a soft and penetrable interface that we solve using computer simulations of Navier-Stokes flows. The literature contains studies of similar isoviscous fluid systems, where the two fluids have the same viscosity. Here we extend this to the more general case where they have different viscosities. We investigate the effect of the fluid viscosity ratio on the movement patterns of microswimmers. We find that swimmers systematically reorientate towards the region containing the lower viscosity fluid. Ultimately this is expected to drive the swimmers to behave as if they are more inclined to swim in low viscosity fluids. Furthermore, in addition to the types of swimming already reported in the isoviscous system, i.e. bouncing, sliding and penetrating, we observed a hovering motion, in which strong pullers swim parallel to the interface with a certain distance, which is consistent with the dynamics of such swimmers near the solid wall.

cond-mat.soft↗

Dynamics of microswimmers near a soft penetrable interface

Few simulations exist for microswimmers near deformable interfaces. Here, we present numerical simulations of the hydrodynamic flows associated with a single microswimmer embedded in a binary fluid mixture. The two fluids demix, separated by a penetrable and deformable interface that we assume to be initially prepared in its planar ground-state. We find that the microswimmer can either penetrate the interface, move parallel to it or bounce back off it. We analyze how the trajectory depends on the swimmer type (pusher/puller) and the angle of incidence with respect to the interface. Our simulations are performed in a system with periodic boundary conditions, corresponding to an infinite array of fluid interfaces. A puller reaches a steady state in which it either swims parallel to the interface or selects a perpendicular orientation, repeatedly penetrating through the interface. In contrast, a pusher follows a bouncing trajectory between two interfaces. We discuss several examples in biology in which swimmers penetrate soft interfaces. Our work can be seen as a highly simplified model of such processes.

cond-mat.soft↗

Collective Motion of Quincke Rollers with Fully Resolved Hydrodynamics

A Quincke roller is a unique active particle that can run and tumble freely on a flat plate due to the torque generated by a uniform DC electric field applied perpendicular to the plate. A system involving many such particles exhibits a variety of collective dynamics, such as the disordered gas, polar liquid, and active crystal states. We performed direct numerical simulations of a three-dimensional system containing many self-rotating particles to explicitly resolve the hydrodynamic interactions among rotating particles. The collective motion depends on the magnitude of the dipole moments induced on the dielectric particles, the area fraction of particles, and the strength of interparticle attraction. We find that the highly ordered polar liquid state is destabilized by the hydrodynamic interaction between rotating particles at high densities: the near-field lubrication interaction becomes dominant over far-field effects as the interparticle separation becomes shorter.

cond-mat.soft↗

Nash Neural Networks : Inferring Utilities from Optimal Behaviour

We propose Nash Neural Networks ($N^3$) as a new type of Physics Informed Neural Network that is able to infer the underlying utility from observations of how rational individuals behave in a differential game with a Nash equilibrium. We assume that the dynamics for both the population and the individual are known, but not the payoff function, which specifies the cost per unit time of being in any particular state. We construct our network in such a way that the Euler-Lagrange equations of the corresponding optimal control problem are satisfied and the optimal control is self-consistently determined. In this way, we are able to learn the unknown payoff function in an unsupervised manner. We have applied the $N^3$ to study the optimal behaviour during epidemics, in which individuals can choose to socially distance depending on the state of the pandemic and the cost of being infected. Training our network against synthetic data for a simple SIR model, we showed that it is possible to accurately reproduce the hidden payoff function, in such a way that the game dynamics are respected. Our approach will have far-reaching applications, as it allows one to infer utilities from behavioural data, and can thus be applied to study a wide array of problems in science, engineering, economics and government planning.

cs.LG↗

Learning the constitutive relation of polymeric flows with memory

We develop a learning strategy to infer the constitutive relation for the stress of polymeric flows with memory. We make no assumptions regarding the functional form of the constitutive relations, except that they should be expressible in differential form as a function of the local stress- and strain-rate tensors. In particular, we use a Gaussian Process regression to infer the constitutive relations from stress trajectories generated from small-scale (fixed strain-rate) microscopic polymer simulations. For simplicity, a Hookean dumbbell representation is used as a microscopic model, but the method itself can be generalized to incorporate more realistic descriptions. The learned constitutive relation is then used to perform macroscopic flow simulations, allowing us to update the stress distribution in the fluid in a manner that accounts for the microscopic polymer dynamics. The results using the learned constitutive relation are in excellent agreement with full Multi-Scale Simulations, which directly couple micro/macro degrees of freedom, as well as the exact analytical solution given by the Maxwell constitutive relation. We are able to fully capture the history dependence of the flow, as well as the elastic effects in the fluid. We expect the proposed learning/simulation approach to be used not only to study the dynamics of entangled polymer flows, but also for the complex dynamics of other Soft Matter systems, which possess a similar hierarchy of length- and time-scales.

physics.comp-ph↗

Control of cell colony growth by contact inhibition

We investigate the dynamics of a colony of crawling, proliferating cells with a minimal, mechanical cell model. The cells consist of two disks, modelling the cell body and a pseudopod, connected by a finite extensible spring. The cells exhibit locomotion due to a linear coupling of the motility force to the cell extension. With a simple mechanism for contact inhibition of proliferation, we find the typical regimes of colony growth, with exponential growth at short times turning into sub-exponential growth at long times. In the latter regime the colony boundary moves outwards with a constant speed. We identify simple scaling relations for both regimes and the crossover between them. We find that the shape of the cells (the ratio between the two disk's radii) determines the efficiency with which cells orient themselves away from the colony. The better the cells are at aligning themselves away from the colony, the faster the colony expands.

q-bio.CB↗

Spontaneous Spatiotemporal Ordering of Shape Oscillations Enhances Cell Migration

The migration of cells is relevant for processes such as morphogenesis, wound healing, and invasion of cancer cells. In order to move, single cells deform cyclically. However, it is not understood how these shape oscillations influence collective properties. Here we demonstrate, using numerical simulations, that the interplay of directed motion, shape oscillations, and excluded volume enables cells to locally "synchronize" their motion and thus enhance collective migration. Our model captures elongation and contraction of crawling ameboid cells controlled by an internal clock with a fixed period, mimicking the internal cycle of biological cells. We show that shape oscillations are crucial for local rearrangements that induce ordering of internal clocks between neighboring cells even in the absence of signaling and regularization. Our findings reveal a novel, purely physical mechanism through which the internal dynamics of cells influences their collective behavior, which is distinct from well known mechanisms like chemotaxis, cell division, and cell-cell adhesion.

physics.bio-ph↗

Modeling the Mechanosensitivity of Fast-Crawling Cells on Cyclically Stretched Substrates

The mechanosensitivity of cells, which determines how they are able to respond to mechanical signals received from their environment, is crucial for the functioning of all biological systems. In experiments, cells placed on cyclically stretched substrates have been shown to reorient in a direction that depends not only on the type of cell, but also on the mechanical properties of the substrate, and the amplitude and rate of stretching. However, the underlying biochemical and mechanical mechanisms responsible for this realignment are still not completely understood. In this study, we introduce a computational model for fast crawling on cyclically stretched substrates that accounts for the sub-cellular processes responsible for the cell shape and motility, as well as the coupling to the substrate through the focal adhesion sites. In particular, we focus on the role of the focal adhesion dynamics, and show that the reorientation under cyclic stretching is strongly dependent on the frequency, as has been observed experimentally. Furthermore, we show that an asymmetry during the loading and unloading phases of the stretching, whether coming from the response of the cell itself, or from the stretching protocol, can be used to selectively align the cells in either the parallel or perpendicular directions.

physics.bio-ph↗

Noise induced phase separation in active systems: Creating patterns with noise

We study the flocking and pattern formations of active particles with a Vicsek-like model that includes a configuration dependent noise term. In particular, we couple the strength of the noise with both the local density and orientation of neighboring particles. Our results show that such a configuration dependent noise can lead to the appearance of large-scale ordered and disordered patterns, without the need for any complex alignment interactions. In particular, we obtain an ordered band or line state and a disordered active cluster, similar to that seen in the case of motility induced phase separation.

physics.bio-ph↗

Hydrodynamic Interactions of Self-Propelled Swimmers

The hydrodynamic interactions of a suspension of self-propelled particles are studied using a direct numerical simulation method which simultaneously solves for the host fluid and the swimming particles. A modified version of the "Smoothed Profile" method (SPM) is developed to simulate microswimmers as squirmers, which are spherical particles with a specified surface-tangential slip velocity between the particles and the fluid. This simplified swimming model allows one to represent different types of propulsion (pullers and pushers) and is thus ideal to study the hydrodynamic interactions among swimmers. We use the SPM to study the diffusive behavior which arises due to the swimming motion of the particles, and show that there are two basic mechanisms responsible for this phenomena: the hydrodynamic interactions caused by the squirming motion of the particles, and the particle-particle collisions. This dual nature gives rise to two distinct time- and length- scales, and thus to two diffusion coefficients, which we obtain by a suitable analysis of the swimming motion. We show that the collisions between swimmers can be interpreted in terms of binary collisions, in which the effective collision radius is reduced due to the collision dynamics of swimming particles in viscous fluids. At short time-scales, the dynamics of the swimmer is analogous to that of an inert tracer particle in a swimming suspension, in which the diffusive motion is caused by fluid-particle collisions. Our results, along with the simulation method we have introduced, will allow us to gain a better understanding of the complex hydrodynamic interactions of self-propelled swimmers.

cond-mat.soft↗

A transferable ab-initio based force field for aqueous ions

We present a new polarizable force field for aqueous ions (Li+, Na+, K+, Rb+, Cs+, Mg2+, Ca2+, Sr2+ and Cl-) derived from condensed phase ab-initio calculations. We use Maximally Localized Wannier Functions together with a generalized force and dipole-matching procedure to determine the whole set of parameters. Experimental data is then used only for validation purposes and a good agreement is obtained for structural, dynamic and thermodynamic properties. The same procedure applied to crystalline phases allows to parametrize the interaction between cations and the chloride anion. Finally, we illustrate the good transferability of the force field to other thermodynamic conditions by investigating concentrated solutions.

physics.chem-ph↗