Moteur physique

Résumé

Un moteur physique est, en informatique, une bibliothèque logicielle indépendante appliquée à la résolution de problèmes de la mécanique classique. Les résolutions typiques sont les collisions, la chute des corps, les forces, la cinétique, etc. Les moteurs physiques sont principalement utilisés dans des simulations scientifiques et dans les jeux vidéo. Moteurs libres Certains sont également libres pour l'utilisation commerciale, à vérifier bibliothèque par bibliothèque. 2D

Box2D (Licence Zlib)
Chipmunk (C, C++, Ruby, Python, OCaml. Licence MIT)

Bullet (C++, OpenCL, Licence zlib), également porté en javascript via différents compilateur C++ to js dont ECMAScript et en Java (nommé JBullet)
Dynamechs
Dynamo (C++, Licence GPL)
Fast CCD collision detection
IBDS
Jiggle
Newton Game Dynamics (Licence zlib)
ODE (C, C++, Python, OCaml, Java avec ODE4J. Double licence LGPL ou BSD)
OpenTissue
Physsim
SOLID (Licences GPL/QGPL + l

Source officielle

https://fr.wikipedia.org/wiki/Moteur_physique

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (21)

Publications associées

Chargement

Personnes associées (1)

Personnes associées

Chargement

Unités associées (1)

Unités associées

Chargement

Concepts associés (14)

Concepts associés

Chargement

Cours associés (3)

Cours associés

Chargement

Séances de cours associées (3)

Séances de cours associées

Chargement

Status of the ALICE CERN Analysis Facility

Marco Meoni

The ALICE experiment at CERN LHC is using a PROOF-enabled cluster for fast physics analysis, detector calibration and reconstruction of small data samples. The current system (CAF - CERN Analysis Facility) consists of some 120 CPU cores and about 45 TB of disk space distributed across the CAF hosts. One of the most important aspects of the data analysis on the CAF is the speed with which it can be carried out. The system is particularly aimed at the prototyping phase of analyses that need a high number of iterations and thus require a short response time. Quasi-online quality assurance of data can be obtained. The paper describes the design principles of the PROOF frame work and presents the current setup, performance tests and usage statistics. Subsets of selected data can be automatically staged in CAF from the Grid storage systems, therefore data distribution and staging techniques are described in depth. A fairshare algorithm to adjust the priorities of concurrently running sessions is also examined. Further more, the adaptation of PROOF to the AliEn/gLite Grid middleware is described. This approach enables a dynamic startup of PROOF nodes worldwide with the purpose to process much larger physics datasets.

Iop Publishing Ltd, Dirac House, Temple Back, Bristol Bs1 6Be, England2010

Chargement

The HLT (High-Level Trigger) group of the ALICE experiment at the LHC has prepared a virtual Parallel ROOT Facility (PROOF) enabled cluster (HAF - HLT Analysis Facility) for fast physics analysis, detector calibration and reconstruction of data samples. The HLT-Cluster currently consists of 2860 CPU cores and 175TB of storage. Its purpose is the online filtering of the relevant part of data produced by the particle detector. However, data taking is not running continuously and exploiting unused cluster resources for other applications is highly desirable and improves the usage-cost ratio of the HLT cluster. As such, unused computing resources are dedicated to a PROOF-enabled virtual cluster available to the entire collaboration. This setup is especially aimed at the prototyping phase of analyses that need a high number of development iterations and a short response time, e. g. tuning of analysis cuts, calibration and alignment. HAF machines are enabled and disabled upon user request to start or complete analysis tasks. This is achieved by a virtual machine scheduling framework which dynamically assigns and migrates virtual machines running PROOF workers to unused physical resources. Using this approach we extend the HLT usage scheme to running both online and offline computing, thereby optimizing the resource usage.

Iop Publishing Ltd, Dirac House, Temple Back, Bristol Bs1 6Be, England2011

Chargement

GarNet: A Two-Stream Network for Fast and Accurate 3D Cloth Draping

Victor Constantin, Pascal Fua, Erhan Gündogdu, Mathieu Salzmann

While Physics-Based Simulation (PBS) can accurately drape a 3D garment on a 3D body, it remains too costly for real-time applications, such as virtual try-on. By contrast, inference in a deep network, requiring a single forward pass, is much faster. Taking advantage of this, we propose a novel architecture to fit a 3D garment template to a 3D body. Specifically, we build upon the recent progress in 3D point cloud processing with deep networks to extract garment features at varying levels of detail, including point-wise, patch-wise and global features. We fuse these features with those extracted in parallel from the 3D body, so as to model the cloth-body interactions. The resulting two-stream architecture, which we call as GarNet, is trained using a loss function inspired by physics-based modeling, and delivers visually plausible garment shapes whose 3D points are, on average, less than 1 cm away from those of a PBS method, while running 100 times faster. Moreover, the proposed method can model various garment types with different cutting patterns when parameters of those patterns are given as input to the network.

IEEE2019

Chargement