Operator learning for predicting multiscale bubble growth dynamics.

Simulating and predicting multiscale problems that couple multiple physics and dynamics across many orders of spatiotemporal scales is a great challenge that has not been investigated systematically by deep neural networks (DNNs). Herein, we develop a framework based on operator regression, the so-called deep operator network (DeepONet), with the long-term objective to simplify multiscale modeling by avoiding the fragile and time-consuming "hand-shaking" interface algorithms for stitching together heterogeneous descriptions of multiscale phenomena. To this end, as a first step, we investigate if a DeepONet can learn the dynamics of different scale regimes, one at the deterministic macroscale and the other at the stochastic microscale regime with inherent thermal fluctuations. Specifically, we test the effectiveness and accuracy of the DeepONet in predicting multirate bubble growth dynamics, which is described by a Rayleigh-Plesset (R-P) equation at the macroscale and modeled as a stochastic nucleation and cavitation process at the microscale by dissipative particle dynamics (DPD). First, we generate data using the R-P equation for multirate bubble growth dynamics caused by randomly time-varying liquid pressures drawn from Gaussian random fields (GRFs). Our results show that properly trained DeepONets can accurately predict the macroscale bubble growth dynamics and can outperform long short-term memory networks. We also demonstrate that the DeepONet can extrapolate accurately outside the input distribution using only very few new measurements. Subsequently, we train the DeepONet with DPD data corresponding to stochastic bubble growth dynamics. Although the DPD data are noisy and we only collect sparse data points on the trajectories, the trained DeepONet model is able to predict accurately the mean bubble dynamics for time-varying GRF pressures. Taken together, our findings demonstrate that DeepONets can be employed to unify the macroscale and microscale models of the multirate bubble growth problem, hence providing new insight into the role of operator regression via DNNs in tackling realistic multiscale problems and in simplifying modeling with heterogeneous descriptions.

Tuning Drop Motion by Chemical Chessboard-Patterned Surfaces: A Many-Body Dissipative Particle Dynamics Study.

Controlling the motion of liquid drops on the solid surface has broad technological implications. In this study, the many-body dissipative particle dynamics (MDPD) was employed to study the drop behaviors on chemical chessboard-patterned surfaces formed by square or triangular tiles. The scaling relationship of the model was established based on the surface tension, viscosity, and density of a real fluid, and an improved contact angle measurement technique was introduced to the MDPD system. For drops on a horizontal plane with different tile sizes, the equilibrium morphology was examined. The critical Bond number, that is, the critical dimensionless force which is required to unpin the drop, was found strongly affected by the size and the shape of the tiles. Once the droplet begins to move, the tile pattern and the size strongly affect the velocity fluctuation while weakly affect the average velocity. Interestingly, besides the common straight forward path, two more route patterns (zigzag and oblique) were observed by only tuning the tile angle, indicating that the advancing routes of the drop may vary according to the tile angle. To the author's knowledge, this phenomenon has not been reported in the literature. This study provides a valuable tool to explore the possibility of passive control of the drop's motion by energy-free chemical heterogeneous surfaces and thus is helpful for engineers to design a surface that could manipulate the drop motion without external energy.

Unraveling the motion and deformation characteristics of red blood cells in a deterministic lateral displacement device.

Deterministic Lateral Displacement (DLD) device has gained widespread recognition and trusted for filtering blood cells. However, there remains a crucial need to explore the complex interplay between deformable cells and flow within the DLD device to improve its design. This paper presents an approach utilizing a mesoscopic cell-level numerical model based on dissipative particle dynamics to effectively capture this complex phenomenon. To establish the model's credibility, a series of numerical simulations were conducted and the numerical results were validated with nominal experimental data from the literature. These include single cell stretching experiment, comparisons of the morphological characteristics of cells in DLD, and comparison the specific row-shift fraction of DLD required to initiate the zigzag mode. Additionally, we investigate the effect of cell rigidity, which serves as an indicator of cell health, on average flow velocity, trajectory, and asphericity. Moreover, we extend the existing theory of predicting zigzag mode for solid spherical particles to encompass the behavior of red blood cells. To achieve this, we introduce a new concept of effective diameter and demonstrate its applicability in providing highly accurate predictions across a wide range of conditions.

A machine learning model that outperforms conventional global subseasonal forecast models.

Skillful subseasonal forecasts are crucial for various sectors of society but pose a grand scientific challenge. Recently, machine learning-based weather forecasting models outperform the most successful numerical weather predictions generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), but have not yet surpassed conventional models at subseasonal timescales. This paper introduces FuXi Subseasonal-to-Seasonal (FuXi-S2S), a machine learning model that provides global daily mean forecasts up to 42 days, encompassing five upper-air atmospheric variables at 13 pressure levels and 11 surface variables. FuXi-S2S, trained on 72 years of daily statistics from ECMWF ERA5 reanalysis data, outperforms the ECMWF's state-of-the-art Subseasonal-to-Seasonal model in ensemble mean and ensemble forecasts for total precipitation and outgoing longwave radiation, notably enhancing global precipitation forecast. The improved performance of FuXi-S2S can be primarily attributed to its superior capability to capture forecast uncertainty and accurately predict the Madden-Julian Oscillation (MJO), extending the skillful MJO prediction from 30 days to 36 days. Moreover, FuXi-S2S not only captures realistic teleconnections associated with the MJO but also emerges as a valuable tool for discovering precursor signals, offering researchers insights and potentially establishing a new paradigm in Earth system science research.

Simulation of a tumor cell flowing through a symmetric bifurcated microvessel.

Microvessel bifurcations serve as the major sites of tumor cell adhesion and further extravasation. In this study, the movement, deformation, and adhesion of a circulating tumor cell flowing in a symmetric microvessel with diverging and converging bifurcations were simulated by dissipative particle dynamics combined with a spring-based network model. Effects of the initial position of the CTC, externally-applied acceleration and the presence of RBCs on the motion of the CTC were investigated. The results demonstrated that the CTC released at the centerline of the parent vessel would attach to the vessel wall when arriving at the apex of diverging bifurcation and slide into the daughter branch determined by its centroid deflection and finally form firm adhesion at relatively lower flow rates. As the external acceleration increases, the increasing shear force enlarges the contact area for the adherent CTC on the one hand and reduces the residence time on the other hand. With the presence of RBCs in the bloodstream, the collision between the adherent tumor cell at the diverging bifurcation and flowing RBCs promotes the firm adhesion of CTC at lower flow rates.

Integrating blood cell mechanics, platelet adhesive dynamics and coagulation cascade for modelling thrombus formation in normal and diabetic blood.

Normal haemostasis is an important physiological mechanism that prevents excessive bleeding during trauma, whereas the pathological thrombosis especially in diabetics leads to increased incidence of heart attacks and strokes as well as peripheral vascular events. In this work, we propose a new multiscale framework that integrates seamlessly four key components of blood clotting, namely transport of coagulation factors, coagulation kinetics, blood cell mechanics and platelet adhesive dynamics, to model the development of thrombi under physiological and pathological conditions. We implement this framework to simulate platelet adhesion due to the exposure of tissue factor in a three-dimensional microchannel. Our results show that our model can simulate thrombin-mediated platelet activation in the flowing blood, resulting in platelet adhesion to the injury site of the channel wall. Furthermore, we simulate platelet adhesion in diabetic blood, and our results show that both the pathological alterations in the biomechanics of blood cells and changes in the amount of coagulation factors contribute to the excessive platelet adhesion and aggregation in diabetic blood. Taken together, this new framework can be used to probe synergistic mechanisms of thrombus formation under physiological and pathological conditions, and open new directions in modelling complex biological problems that involve several multiscale processes.