Mean-field Differential Equations Research Articles

Forecasting and Predicting Stochastic Agent-Based Model Data with Biologically-Informed Neural Networks.

Collective migration is an important component of many biological processes, including wound healing, tumorigenesis, and embryo development. Spatial agent-based models (ABMs) are often used to model collective migration, but it is challenging to thoroughly predict these models' behavior throughout parameter space due to their random and computationally intensive nature. Modelers often coarse-grain ABM rules into mean-field differential equation (DE) models. While these DE models are fast to simulate, they suffer from poor (or even ill-posed) ABM predictions in some regions of parameter space. In this work, we describe how biologically-informed neural networks (BINNs) can be trained to learn interpretable BINN-guided DE models capable of accurately predicting ABM behavior. In particular, we show that BINN-guided partial DE (PDE) simulations can (1) forecast future spatial ABM data not seen during model training, and (2) predict ABM data at previously-unexplored parameter values. This latter task is achieved by combining BINN-guided PDE simulations with multivariate interpolation. We demonstrate our approach using three case study ABMs of collective migration that imitate cell biology experiments and find that BINN-guided PDEs accurately forecast and predict ABM data with a one-compartment PDE when the mean-field PDE is ill-posed or requires two compartments. This work suggests that BINN-guided PDEs allow modelers to efficiently explore parameter space, which may enable data-driven tasks for ABMs, such as estimating parameters from experimental data. All code and data from our study is available at https://github.com/johnnardini/Forecasting_predicting_ABMs .

A General Epidemic Model and Its Application to Mask Design Considering Different Preferences towards Masks

While most masks have a limited effect on personal protection, how effective are they for collective protection? How to enlighten the design of masks from the perspective of collective dynamics? In this paper, we assume three preferences in the population: (i) never wearing a mask; (ii) wearing a mask if and only if infected; (iii) always wearing a mask. We study the epidemic transmission in an open system within the Susceptible‐Infected‐Recovered (SIR) model framework. We use agent‐based Monte Carlo simulation and mean‐field differential equations to investigate the model, respectively. Ternary heat maps show that wearing masks is always beneficial in curbing the spread of the epidemic. Based on the model, we investigate the potential implications of different mask designs from the perspective of collective dynamics. The results show that strengthening the filterability of the mask from the face to the outside is more effective in most parameter spaces because it acts on individuals with both preferences (ii) and (iii). However, when the fraction of individuals always wearing a mask achieves a critical point, strengthening the filterability from outside to the face becomes more effective because of the emerging hidden reality that the infected individuals become too few to utilize the filterability from their face to outside fully.

Spatial opinion dynamics and the effects of two types of mixing.

Spatially situated opinions that can be held with different degrees of conviction lead to spatiotemporal patterns such as clustering (homophily), polarization, and deadlock. Our goal is to understand how sensitive these patterns are to changes in the local nature of interactions. We introduce two different mixing mechanisms, spatial relocation and nonlocal interaction ("telephoning"), to an earlier fully spatial model (no mixing). Interestingly, the mechanisms that create deadlock in the fully spatial model have the opposite effect when there is a sufficient amount of mixing. With telephoning, not only is polarization and deadlock broken up, but consensus is hastened. The effects of mixing by relocation are even more pronounced. Further insight into these dynamics is obtained for selected parameter regimes via comparison to the mean-field differential equations.

TAIL PROBABILITIES IN QUEUEING PROCESSES

In the study of large scale stochastic networks with resource management, differential equations and mean-field limits are two key techniques. Recent research shows that the expected fraction vector (that is, the tail probability vector) plays a key role in setting up mean-field differential equations. To further apply the technique of tail probability vector to deal with resource management of large scale stochastic networks, this paper discusses tail probabilities in some basic queueing processes including QBD processes, Markov chains of GI/M/1 type and of M/G/1 type, and also provides some effective and efficient algorithms for computing the tail probabilities by means of the matrix-geometric solution, the matrix-iterative solution, the matrix-product solution and the two types of RG-factorizations. Furthermore, we consider four queueing examples: The M/M/1 retrial queue, the M(n)/M(n)/1 queue, the M/M/1 queue with server multiple vacations and the M/M/1 queue with repairable server, where the M/M/1 retrial queue is given a detailed discussion, while the other three examples are analyzed in less detail. Note that the results given in this paper will be very useful in the study of large scale stochastic networks with resource management, including the supermarket models and the work stealing models.

Chemical evolutionary games

Inspired by the use of hybrid cellular automata in modeling cancer, we introduce a generalization of evolutionary games in which cells produce and absorb chemicals, and the chemical concentrations dictate the death rates of cells and their fitnesses. Our long term aim is to understand how the details of the interactions in a system with n species and m chemicals translate into the qualitative behavior of the system. Here, we study two simple 2×2 games with two chemicals and revisit the two and three species versions of the one chemical colicin system studied earlier by Durrett and Levin (1997). We find that in the 2×2 examples, the behavior of our new spatial model can be predicted from that of the mean field differential equation using ideas of Durrett and Levin (1994). However, in the three species colicin model, the system with diffusion does not have the coexistence which occurs in the lattices model in which sites interact with only their nearest neighbors.

Nonequilibrium magnetization reversal by periodic impulsive fields in Ising mean-field dynamics

We studied nonequilibrium magnetization reversal in kinetic Ising ferromagnets driven by a periodic impulsive magnetic field in the mean-field approximation. The mean-field differential equation was solved by the sixth-order Runge–Kutta–Felberg method. The periodicity and strength of the applied impulsive magnetic field play a key role in magnetization reversal. We studied the minimum strength of an impulsive field required for magnetization reversal at any temperature as a function of the periodicity of the impulsive field. At high temperature and small period, this was observed to be linear. The results were compared with those obtained from the Monte Carlo simulation of a three-dimensional Ising ferromagnet.

CRITICAL SLOWING DOWN ALONG THE DYNAMIC PHASE BOUNDARY IN ISING MEANFIELD DYNAMICS

We studied the dynamical phase transition in kinetic Ising ferromagnets driven by oscillating magnetic field in meanfield approximation. The meanfield differential equation was solved by sixth-order Runge–Kutta–Felberg method. The time-averaged magnetization plays the role of the dynamic order parameter. We studied the relaxation behavior of the dynamic order parameter close to the transition temperature, which depends on the amplitude of the applied magnetic field. We observed the critical slowing down along the dynamic phase boundary. We proposed a power law divergence of the relaxation time and estimated the exponent. We also found its dependence on the field amplitude and compared the result with the exact value in limiting case.

Equation-free multiscale computations for a lattice-gas model: coarse-grained bifurcation analysis of the NO+CO reaction on Pt(1 0 0)

Using the recently developed “coarse timestepper” approach (Proc. Nat. Acad. Sci. USA 97 (2000) 9840) we study a lattice-gas model of the NO+CO/Pt(1 0 0) reaction exhibiting macroscopic bistability and kinetic oscillations. Through numerical continuation and stability analysis, we construct one-parameter coarse bifurcation diagrams and contrast the results of mean-field differential equation models with the coarse-grained, expected dynamics of kinetic Monte Carlo (kMC) lattice-gas model simulations. We show how our computational superstructure enables the direct kMC simulator to perform tasks, such as continuation and numerical bifurcation analysis, for which it has not been originally designed. This closure-on-demand approach trades function evaluations with estimation based on short, appropriately initialized kMC simulations. We discuss its scope in complex/multiscale system modeling and simulation.

Cluster diffusion and dissociation in the kinetics of layer growth: An atomic view

Data on diffusion and dissociation of single Pt clusters on Pt(111), available for the first time from field ion microscope observations, are used to make predictions about nanoscale phenomena by solving the mean-field differential equations for growth. In the submonolayer regime, cluster dissociation is found to be much more important than diffusion of clusters in reducing the saturation island density. Cluster dissociation also significantly affects the nucleation of a second layer on top of existing clusters and must be considered on an equal footing with interlayer transport over cluster edges.