Transient thermal modelling of substation connectors by means of dimensionality reduction

This paper proposes a simple, fast and accurate simulation approach based on one-dimensional reduction and the application of the finite difference method (FDM) to determine the temperatures rise in substation connectors. The method discretizes the studied three-dimensional geometry in a finite number of one-dimensional elements or regions in which the energy rate balance is calculated. Although a one-dimensional reduction is applied, to ensure the accuracy of the proposed transient method, it takes into account the three-dimensional geometry of the analyzed system to determine for all analyzed elements and at each time step different parameters such as the incremental resistance of each element or the convective coefficient. The proposed approach allows fulfilling both accuracy and low computational burden criteria, providing similar accuracy than the three-dimensional finite element method but with much lower computational requirements. Experimental results conducted in a high-current laboratory validate the accuracy and effectiveness of the proposed method and its usefulness to design substation connectors and other power devices and components with an optimal thermal behavior.Postprint (published version

FluSI: A novel parallel simulation tool for flapping insect flight using a Fourier method with volume penalization

FluSI, a fully parallel open source software for pseudo-spectral simulations of three-dimensional flapping flight in viscous flows, is presented. It is freely available for non-commercial use under [https://github.com/pseudospectators/FLUSI]. The computational framework runs on high performance computers with distributed memory architectures. The discretization of the three-dimensional incompressible Navier--Stokes equations is based on a Fourier pseudo-spectral method with adaptive time stepping. The complex time varying geometry of insects with rigid flapping wings is handled with the volume penalization method. The modules characterizing the insect geometry, the flight mechanics and the wing kinematics are described. Validation tests for different benchmarks illustrate the efficiency and precision of the approach. Finally, computations of a model insect in the turbulent regime demonstrate the versatility of the software

Algebraic Systems Biology: A Case Study for the Wnt Pathway

Steady state analysis of dynamical systems for biological networks give rise to algebraic varieties in high-dimensional spaces whose study is of interest in their own right. We demonstrate this for the shuttle model of the Wnt signaling pathway. Here the variety is described by a polynomial system in 19 unknowns and 36 parameters. Current methods from computational algebraic geometry and combinatorics are applied to analyze this model.Comment: 24 pages, 2 figure

Learning Generative Models with Sinkhorn Divergences

The ability to compare two degenerate probability distributions (i.e. two probability distributions supported on two distinct low-dimensional manifolds living in a much higher-dimensional space) is a crucial problem arising in the estimation of generative models for high-dimensional observations such as those arising in computer vision or natural language. It is known that optimal transport metrics can represent a cure for this problem, since they were specifically designed as an alternative to information divergences to handle such problematic scenarios. Unfortunately, training generative machines using OT raises formidable computational and statistical challenges, because of (i) the computational burden of evaluating OT losses, (ii) the instability and lack of smoothness of these losses, (iii) the difficulty to estimate robustly these losses and their gradients in high dimension. This paper presents the first tractable computational method to train large scale generative models using an optimal transport loss, and tackles these three issues by relying on two key ideas: (a) entropic smoothing, which turns the original OT loss into one that can be computed using Sinkhorn fixed point iterations; (b) algorithmic (automatic) differentiation of these iterations. These two approximations result in a robust and differentiable approximation of the OT loss with streamlined GPU execution. Entropic smoothing generates a family of losses interpolating between Wasserstein (OT) and Maximum Mean Discrepancy (MMD), thus allowing to find a sweet spot leveraging the geometry of OT and the favorable high-dimensional sample complexity of MMD which comes with unbiased gradient estimates. The resulting computational architecture complements nicely standard deep network generative models by a stack of extra layers implementing the loss function

A Simple Passive Scalar Advection-Diffusion Model

This paper presents a simple, one-dimensional model of a randomly advected passive scalar. The model exhibits anomalous inertial range scaling for the structure functions constructed from scalar differences. The model provides a simple computational test for recent ideas regarding closure and scaling for randomly advected passive scalars. Results suggest that high order structure function scaling depends on the largest velocity eddy size, and hence scaling exponents may be geometry-dependent and non-universal.Comment: 30 pages, 11 figure

Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional Archive of Phenotypic Elites Algorithm

The recently introduced Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) is an evolutionary algorithm capable of producing a large archive of diverse, high-performing solutions in a single run. It works by discretizing a continuous feature space into unique regions according to the desired discretization per dimension. While simple, this algorithm has a main drawback: it cannot scale to high-dimensional feature spaces since the number of regions increase exponentially with the number of dimensions. In this paper, we address this limitation by introducing a simple extension of MAP-Elites that has a constant, pre-defined number of regions irrespective of the dimensionality of the feature space. Our main insight is that methods from computational geometry could partition a high-dimensional space into well-spread geometric regions. In particular, our algorithm uses a centroidal Voronoi tessellation (CVT) to divide the feature space into a desired number of regions; it then places every generated individual in its closest region, replacing a less fit one if the region is already occupied. We demonstrate the effectiveness of the new "CVT-MAP-Elites" algorithm in high-dimensional feature spaces through comparisons against MAP-Elites in maze navigation and hexapod locomotion tasks