Physics-Guided Deep Learning for Drag Force Prediction in Dense Fluid-Particulate Systems
نویسندگان
چکیده
منابع مشابه
Guided Deep Reinforcement Learning for Swarm Systems
In this paper, we investigate how to learn to control a group of cooperative agents with limited sensing capabilities such as robot swarms. The agents have only very basic sensor capabilities, yet in a group they can accomplish sophisticated tasks, such as distributed assembly or search and rescue tasks. Learning a policy for a group of agents is difficult due to distributed partial observabili...
متن کاملDeep learning-based CAD systems for mammography: A review article
Breast cancer is one of the most common types of cancer in women. Screening mammography is a low‑dose X‑ray examination of breasts, which is conducted to detect breast cancer at early stages when the cancerous tumor is too small to be felt as a lump. Screening mammography is conducted for women with no symptoms of breast cancer, for early detection of cancer when the cancer is most treatable an...
متن کاملEvolution of force networks in dense particulate media.
We discuss sets of measures with the goal of describing dynamical properties of force networks in dense particulate systems. The presented approach is based on persistent homology and allows for extracting precise, quantitative measures that describe the evolution of geometric features of the interparticle forces, without necessarily considering the details related to individual contacts betwee...
متن کاملOpenMP parallelism for fluid and fluid-particulate systems
0167-8191/$ see front matter 2012 Elsevier B.V http://dx.doi.org/10.1016/j.parco.2012.05.005 ⇑ Corresponding author. Tel.: +1 540 231 9975; fa E-mail address: [email protected] (D. Tafti). In order to exploit the flexibility of OpenMP in parallelizing large scale multi-physics applications where different modes of parallelism are needed for efficient computation, it is first necessary to be able to...
متن کاملAttention Guided Deep Imitation Learning
When a learning agent attempts to imitate human visuomotor behaviors, it may benefit from knowing the human demonstrator’s visual attention. Such information could clarify the goal of the demonstrator, i.e., the object being attended is the most likely target of the current action. Hence it could help the agent better infer and learn the demonstrator’s underlying state representation for decisi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Big Data
سال: 2020
ISSN: 2167-6461,2167-647X
DOI: 10.1089/big.2020.0071