Efficient Generation of Multimodal Fluid Simulation Data

Applying the representational power of machine learning to the prediction of complex fluid dynamics has been a relevant subject of study for years. However, the amount of available fluid simulation data does not match the notoriously high requirements of machine learning methods. Researchers have typically addressed this issue by generating their own datasets, preventing a consistent evaluation of their proposed approaches. Our work introduces a generation procedure for synthetic multi-modal fluid simulations datasets. By leveraging a GPU implementation, our procedure is also efficient enough that no data needs to be exchanged between users, except for configuration files required to reproduce the dataset. Furthermore, our procedure allows multiple modalities (generating both geometry and photorealistic renderings) and is general enough for it to be applied to various tasks in data-driven fluid simulation. We then employ our framework to generate a set of thoughtfully designed benchmark datasets, which attempt to span specific fluid simulation scenarios in a meaningful way. The properties of our contributions are demonstrated by evaluating recently published algorithms for the neural fluid simulation and fluid inverse rendering tasks using our benchmark datasets. Our contribution aims to fulfill the community's need for standardized benchmarks, fostering research that is more reproducible and robust than previous endeavors.Comment: 10 pages, 7 figure

Scalable geometry processing for computer graphics applications

This thesis explores and investigates scalable solutions, grounded on geometry processing and differential geometry concepts, to different computer graphics tasks. My Ph.D. path gave me the opportunity to probe many research topics in the field of computer graphics, as well as delve into mathematical and computational problems. As a summary of my research activity, this thesis echoes my exploration, collecting results from different areas of computer graphics and computational geometry. From novel unified frameworks in spectral geometry to procedural texturing techniques, simulations, and matrix multiplication algorithms, all the discussed topics find their communion in the idea of providing geometry processing solutions made to scale for large volumes of data

Massive Uniform Mesh Decimation via a Fast Intrinsic Delaunay Triangulation

Triangular meshes are still today the data structure at the main foundations of many computer graphics applications. With the increasing demand in content variety, a lot of effort has been and is being put into developing new algorithms to automatically generate and edit geometric assets, with a particular focus on 3D scans. However, this kind of content is often generated with a dramatically high resolution, making it impractical for a large variety of tasks. Furthermore, procedural assets and 3D scans largely suffer from poor geometry quality, which makes them unsuitable in various applications. We propose a new efficient technique for massively decimating dense meshes with high vertex count very quickly. The proposed method relies on a fast algorithm for computing geodesic farthest point sampling and Voronoi partitioning, and generates simplified meshes with high-quality uniform triangulations

Fluid Dynamics Network: Topology-Agnostic 4D Reconstruction via Fluid Dynamics Priors

Representing 3D surfaces as level sets of continuous functions over R3 is the common denominator of neural implicit representations, which recently enabled remarkable progress in geometric deep learning and computer vision tasks. In order to represent 3D motion within this framework, it is often assumed (either explicitly or implicitly) that the transformations which a surface may undergo are homeomorphic: this is not necessarily true, for instance, in the case of fluid dynamics. In order to represent more general classes of deformations, we propose to apply this theoretical framework as regularizers for the optimization of simple 4D implicit functions (such as signed distance fields). We show that our representation is capable of capturing both homeomorphic and topology-changing deformations, while also defining correspondences over the continuously-reconstructed surfaces

Geografie che cambiano: Eugeo 2013

L’articolo ripercorre l’esperienza organizzativa del IV Congresso Eugeo, l’associazione delle società geografiche europee, che si è svolto a Roma dal 5 al 7 settembre 2013 con il titolo Europe, what’s next? Changing geographies and geographies of change. Dopo una breve rassegna dei contenuti scientifici del congresso, ci si sofferma sulla strategia comunicativa e sul metodo organizzativo adottato: un modello decentrato e aperto al contributo di tutti, che ha favorito a nostro avviso un dialogo particolarmente proficuo e orizzontale.The article offers an overview of the IV Eugeo Congress, the organization of European geographical societies, held in Rome on September 5-7 2013 with the title Europe, what’s next? Changing geographies and geographies of change. After a brief review of the scientific contents of the congress, we focus on the communicative strategy and on the organization method that has been adopted: a decentred and open model that, in our opinion, has contributed to set the conditions for a fruitful and horizontal scientific dialogue

Efficiently Parallelizable Strassen-Based Multiplication of a Matrix by its Transpose

The multiplication of a matrix by its transpose, ATA, appears as an intermediate operation in the solution of a wide set of problems. In this paper, we propose a new cache-oblivious algorithm (AtA) for computing this product, based upon the classical Strassen algorithm as a sub-routine. In particular, we decrease the computational cost to the time required by Strassen’s algorithm, amounting to floating point operations. AtA works for generic rectangular matrices, and exploits the peculiar symmetry of the resulting product matrix for saving memory. In addition, we provide an extensive implementation study of AtA in a shared memory system, and extend its applicability to a distributed environment. To support our findings, we compare our algorithm with state-of-the-art solutions specialized in the computation of ATA. Our experiments highlight good scalability with respect to both the matrix size and the number of involved processes, as well as favorable performance for both the parallel paradigms and the sequential implementation, when compared with other methods in the literature