Transport-Based Neural Style Transfer for Smoke Simulations

Artistically controlling fluids has always been a challenging task. Optimization techniques rely on approximating simulation states towards target velocity or density field configurations, which are often handcrafted by artists to indirectly control smoke dynamics. Patch synthesis techniques transfer image textures or simulation features to a target flow field. However, these are either limited to adding structural patterns or augmenting coarse flows with turbulent structures, and hence cannot capture the full spectrum of different styles and semantically complex structures. In this paper, we propose the first Transport-based Neural Style Transfer (TNST) algorithm for volumetric smoke data. Our method is able to transfer features from natural images to smoke simulations, enabling general content-aware manipulations ranging from simple patterns to intricate motifs. The proposed algorithm is physically inspired, since it computes the density transport from a source input smoke to a desired target configuration. Our transport-based approach allows direct control over the divergence of the stylization velocity field by optimizing incompressible and irrotational potentials that transport smoke towards stylization. Temporal consistency is ensured by transporting and aligning subsequent stylized velocities, and 3D reconstructions are computed by seamlessly merging stylizations from different camera viewpoints.Comment: ACM Transaction on Graphics (SIGGRAPH ASIA 2019), additional materials: http://www.byungsoo.me/project/neural-flow-styl

Deep Fluids: A Generative Network for Parameterized Fluid Simulations

This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters. A convolutional neural network is trained on a collection of discrete, parameterizable fluid simulation velocity fields. Due to the capability of deep learning architectures to learn representative features of the data, our generative model is able to accurately approximate the training data set, while providing plausible interpolated in-betweens. The proposed generative model is optimized for fluids by a novel loss function that guarantees divergence-free velocity fields at all times. In addition, we demonstrate that we can handle complex parameterizations in reduced spaces, and advance simulations in time by integrating in the latent space with a second network. Our method models a wide variety of fluid behaviors, thus enabling applications such as fast construction of simulations, interpolation of fluids with different parameters, time re-sampling, latent space simulations, and compression of fluid simulation data. Reconstructed velocity fields are generated up to 700x faster than re-simulating the data with the underlying CPU solver, while achieving compression rates of up to 1300x.Comment: Computer Graphics Forum (Proceedings of EUROGRAPHICS 2019), additional materials: http://www.byungsoo.me/project/deep-fluids

Curl-Flow: Boundary-Respecting Pointwise Incompressible Velocity Interpolation for Grid-Based Fluids

We propose to augment standard grid-based fluid solvers with pointwise divergence-free velocity interpolation, thereby ensuring exact incompressibility down to the sub-cell level. Our method takes as input a discretely divergence-free velocity field generated by a staggered grid pressure projection, and first recovers a corresponding discrete vector potential. Instead of solving a costly vector Poisson problem for the potential, we develop a fast parallel sweeping strategy to find a candidate potential and apply a gauge transformation to enforce the Coulomb gauge condition and thereby make it numerically smooth. Interpolating this discrete potential generates a pointwise vector potential whose analytical curl is a pointwise incompressible velocity field. Our method further supports irregular solid geometry through the use of level set-based cut-cells and a novel Curl-Noise-inspired potential ramping procedure that simultaneously offers strictly non-penetrating velocities and incompressibility. Experimental comparisons demonstrate that the vector potential reconstruction procedure at the heart of our approach is consistently faster than prior such reconstruction schemes, especially those that solve vector Poisson problems. Moreover, in exchange for its modest extra cost, our overall Curl-Flow framework produces significantly improved particle trajectories that closely respect irregular obstacles, do not suffer from spurious sources or sinks, and yield superior particle distributions over time

Neural Smoke Stylization with Color Transfer

Artistically controlling fluid simulations requires a large amount of manual work by an artist. The recently presented transportbased neural style transfer approach simplifies workflows as it transfers the style of arbitrary input images onto 3D smoke simulations. However, the method only modifies the shape of the fluid but omits color information. In this work, we therefore extend the previous approach to obtain a complete pipeline for transferring shape and color information onto 2D and 3D smoke simulations with neural networks. Our results demonstrate that our method successfully transfers colored style features consistently in space and time to smoke data for different input textures.Comment: Submitted to Eurographics202

Preserving Geometry and Topology for Fluid Flows with Thin Obstacles and Narrow Gaps

© ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Azevedo, V. C., Batty, C., & Oliveira, M. M. (2016). Preserving Geometry and Topology for Fluid Flows with Thin Obstacles and Narrow Gaps. Acm Transactions on Graphics, 35(4), 97. https://doi.org/10.1145/2897824.292591Fluid animation methods based on Eulerian grids have long struggled to resolve flows involving narrow gaps and thin solid features. Past approaches have artificially inflated or voxelized boundaries, although this sacrifices the correct geometry and topology of the fluid domain and prevents flow through narrow regions. We present a boundary-respecting fluid simulator that overcomes these challenges. Our solution is to intersect the solid boundary geometry with the cells of a background regular grid to generate a topologically correct, boundary-conforming cut-cell mesh. We extend both pressure projection and velocity advection to support this enhanced grid structure. For pressure projection, we introduce a general graph-based scheme that properly preserves discrete incompressibility even in thin and topologically complex flow regions, while nevertheless yielding symmetric positive definite linear systems. For advection, we exploit polyhedral interpolation to improve the degree to which the flow conforms to irregular and possibly non-convex cell boundaries, and propose a modified PIC/FLIP advection scheme to eliminate the need to inaccurately reinitialize invalid cells that are swept over by moving boundaries. The method naturally extends the standard Eulerian fluid simulation framework, and while we focus on thin boundaries, our contributions are beneficial for volumetric solids as well. Our results demonstrate successful one-way fluid-solid coupling in the presence of thin objects and narrow flow regions even on very coarse grids.Conselho Nacional de Desenvolvimento Científico e Tecnológico, Natural Sciences and Engineering Research Council of Canad

CMRegNet-An interspecies reference database for corynebacterial and mycobacterial regulatory networks

BACKGROUND: Organisms utilize a multitude of mechanisms for responding to changing environmental conditions, maintaining their functional homeostasis and to overcome stress situations. One of the most important mechanisms is transcriptional gene regulation. In-depth study of the transcriptional gene regulatory network can lead to various practical applications, creating a greater understanding of how organisms control their cellular behavior. DESCRIPTION: In this work, we present a new database, CMRegNet for the gene regulatory networks of Corynebacterium glutamicum ATCC 13032 and Mycobacterium tuberculosis H37Rv. We furthermore transferred the known networks of these model organisms to 18 other non-model but phylogenetically close species (target organisms) of the CMNR group. In comparison to other network transfers, for the first time we utilized two model organisms resulting into a more diverse and complete network of the target organisms. CONCLUSION: CMRegNet provides easy access to a total of 3,103 known regulations in C. glutamicum ATCC 13032 and M. tuberculosis H37Rv and to 38,940 evolutionary conserved interactions for 18 non-model species of the CMNR group. This makes CMRegNet to date the most comprehensive database of regulatory interactions of CMNR bacteria. The content of CMRegNet is publicly available online via a web interface found at http://lgcm.icb.ufmg.br/cmregnet

Atmospheric particulate matter from an industrial area as a source of metal nanoparticle contamination in aquatic ecosystems

Air pollution legislation and control worldwide is based on the size of particulate matter (PM) to evaluate the effects on environmental and human health, in which the small diameter particles are considered more dangerous than larger sizes. This study investigates the composition, stability, size and dispersion of atmospheric settleable particulate matter (SePM) in an aqueous system. We aimed to interrogate the changes in the physical properties and characteristics that can contribute to increased metal uptake by aquatic biota. Samples collected in an area influenced by the steel and iron industry were separated into 8 fractions (425 to ≤10 μm) and analysed physically and chemically. Results from ICP-MS and X-ray showed that the PM composition was mainly hematite with 80% of Fe, followed by Al, Mn and Ti. Among 27 elements analysed we found 19 metals, showing emerging metallic contaminants such as Y, Zr, Sn, La, Ba and Bi. Scanning electron microscopy (SEM) showed that SePM fractions are formed by an agglomeration of nanoparticles. Furthermore, dynamic light scattering (DLS), zeta potential and nanoparticle tracking analysis (NTA) demonstrated that SPM were dissociated in water, forming nanoparticles smaller than 200 nm, which can also contribute to water pollution. This study highlights that SePM contamination may be substantially higher than expected under that allowed in atmospheric regulatory frameworks, thereby extending their negative effect to water bodies upon settling, which is an underexplored area of our knowledge. We therefore provide important insights for future investigations on safety regulations involving SePM in the environment, indicating the need to revise the role of SePM, not solely associated with air pollution but also considering their deleterious effects on water resources

Time-Course of Changes in Physiological, Psychological and Performance markers Following a Functional-Fitness Competition

International Journal of Exercise Science 12(3): 904-918, 2019. Functional Fitness Training (FFT) programs are characterized by utilizing a high volume of training and using a variety of high intensity exercises. While FFT are growing in the number of practitioners and popularity, the relationship between physiological biomarkers and subjective scales in the specific context of FFT has not yet been evaluated in the literature. The purpose of the present study was to monitor the time-course response of cytokines (IL-10 and 1L-1b), immune variables (C-reactive protein -CRP and immunoglobulin A-IgA), hormonal milieu (cortisol-C, total testosterone-TT, free testosterone-FT and testosterone/cortisol-T/C ratio), creatine kinase-CK, muscle performance (countermovement jump height) and perceived well-being (WB) following a functional fitness competition. Nine amateur male athletes (age 27.1 ± 4.1 years; training experience 2.2 ± 1.3 years) completed five workouts over three consecutive days of FFT-competition. All variables were measured before, 24 h, 48 h, and 72 h following the last day of competition. The FFT-competition induced a decrease in IL10/IL1bratio approximately 5% after 24h, 21% after 48h and 31% after 72h. Delta T/C ratio remained unchanged during the post-competition period. IgA displayed a significant increase 24h and 72h post FFT-competition. The WB status score was higher 72h after the FFT-competition as compared with pre-competition. The present findings suggest that FFT-competition induces transient changes in some inflammatory and hormonal biomarkers, and perceived well-being seems to be efficient to detect changes in muscle performance