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We present a novel computational framework for the simulation of rigid spherical Janus particle suspensions in Stokes flow. For a wide array of Janus particle types, we show long-range interactions may be resolved using fast, spectrally accurate boundary integral methods. We incorporate this to our rigid body Stokes platform, which resolves hydrodynamic interactions and contact. Our approach features the use of spherical harmonic expansions for spectrally accurate integral operator evaluation, complementarity-based collision resolution, and optimal O (n) scaling with the number of particles when accelerated via fast summation techniques. We demonstrate the versatility of our simulation platform through three test cases involving Janus particle systems prominent in applications: amphiphilic, bipolar electric and phoretic particles. For each test case, we formulate Janus particle interactions in boundary integral form and use our framework to demonstrate examples of self-assembly and complex collective behavior characteristic of these systems. [ABSTRACT FROM AUTHOR]

• Geometry-driven tensorization coupled with the tensor-train decomposition yields a hierarchical compression scheme. • A new streaming data compression scheme, in which batches of simulation data can be efficiently incorporated to a compressed representation as soon as they are generated. • Demonstration of high compression ratios (1 K-10 M fold reductions) of discrete element vehicle-over-soil simulation dataset. We propose a framework for discrete scientific data compression based on the tensor-train (TT) decomposition. Our approach is tailored to handle unstructured output data from discrete element method (DEM) simulations, demonstrating its effectiveness in compressing both raw (e.g. particle position and velocity) and derived (e.g. stress and strain) datasets. We show that geometry-driven "tensorization" coupled with the TT decomposition (known as quantized TT) yields a hierarchical compression scheme, achieving high compression ratios for key variables in these DEM datasets. [ABSTRACT FROM AUTHOR]