A peridynamic based machine learning model for one-dimensional and two-dimensional structures

With the rapid growth of available data and computing resources, using data-driven models is a potential approach in many scientific disciplines and engineering. However, for complex physical phenomena that have limited data, the data-driven models are lacking robustness and fail to provide good predictions. Theory-guided data science is the recent technology that can take advantage of both physics-driven and data-driven models. This study presents a novel peridynamics based machine learning model for one and two-dimensional structures. The linear relationships between the displacement of a material point and displacements of its family members and applied forces are obtained for the machine learning model by using linear regression. The numerical procedure for coupling the peridynamic model and the machine learning model is also provided. The numerical procedure for coupling the peridynamic model and the machine learning model is also provided. The accuracy of the coupled model is verified by considering various examples of a one-dimensional bar and two-dimensional plate. To further demonstrate the capabilities of the coupled model, damage prediction for a plate with a pre-existing crack, a two-dimensional representation of a three-point bending test, and a plate subjected to dynamic load are simulated

Lattice Boltzmann simulations of soft matter systems

This article concerns numerical simulations of the dynamics of particles immersed in a continuum solvent. As prototypical systems, we consider colloidal dispersions of spherical particles and solutions of uncharged polymers. After a brief explanation of the concept of hydrodynamic interactions, we give a general overview over the various simulation methods that have been developed to cope with the resulting computational problems. We then focus on the approach we have developed, which couples a system of particles to a lattice Boltzmann model representing the solvent degrees of freedom. The standard D3Q19 lattice Boltzmann model is derived and explained in depth, followed by a detailed discussion of complementary methods for the coupling of solvent and solute. Colloidal dispersions are best described in terms of extended particles with appropriate boundary conditions at the surfaces, while particles with internal degrees of freedom are easier to simulate as an arrangement of mass points with frictional coupling to the solvent. In both cases, particular care has been taken to simulate thermal fluctuations in a consistent way. The usefulness of this methodology is illustrated by studies from our own research, where the dynamics of colloidal and polymeric systems has been investigated in both equilibrium and nonequilibrium situations.Comment: Review article, submitted to Advances in Polymer Science. 16 figures, 76 page

The conserved dileucine- and tyrosine-based motifs in MLV and MPMV envelope glycoproteins are both important to regulate a common Env intracellular trafficking

BACKGROUND: Retrovirus particles emerge from the assembly of two structural protein components, Gag that is translated as a soluble protein in the cytoplasm of the host cells, and Env, a type I transmembrane protein. Because both components are translated in different intracellular compartments, elucidating the mechanisms of retrovirus assembly thus requires the study of their intracellular trafficking. RESULTS: We used a CD25 (Tac) chimera-based approach to study the trafficking of Moloney murine leukemia virus and Mason-Pfizer monkey virus Env proteins. We found that the cytoplasmic tails (CTs) of both Env conserved two major signals that control a complex intracellular trafficking. A dileucine-based motif controls the sorting of the chimeras from the trans-Golgi network (TGN) toward endosomal compartments. Env proteins then follow a retrograde transport to the TGN due to the action of a tyrosine-based motif. Mutation of either motif induces the mis-localization of the chimeric proteins and both motifs are found to mediate interactions of the viral CTs with clathrin adaptors. CONCLUSION: This data reveals the unexpected complexity of the intracellular trafficking of retrovirus Env proteins that cycle between the TGN and endosomes. Given that Gag proteins hijack endosomal host proteins, our work suggests that the endosomal pathway may be used by retroviruses to ensure proper encountering of viral structural Gag and Env proteins in cells, an essential step of virus assembly

Interacting Particle-Liquid Systems

We present two Euler-Lagrangian simulation methods for particles immersed in fluids described by the Navier-Stokes equation. These implement the coupling between particle and fluid phase by (i) direct integration of the stress tensor on the particle surface discretized according to the grid topology and (ii) by a tracer particle method, which employs the volume force term in the Navier-Stokes equation to emulate "rigid" body motion. Both methods have been parallelized and applied to bulk sedimentation of about 65 000 particles (in one simulation 10 6 particles have been simulated). We also report results for the rheology of shearthinning suspensions, modelled by hydrodynamically interacting particles in shear flows. Aggregation occurs due to attractive, short range forces between the particles. We also address a deficiency of the MPI communication library on the CRAY T3E which had to be resolved to improve the performance of our algorithm