Zobrazeno 1 - 10
of 1 796
pro vyhledávání: '"Benosman, A."'
This paper proposes a learning framework, RoSE-Opt, to achieve robust and efficient analog circuit parameter optimization. RoSE-Opt has two important features. First, it incorporates key domain knowledge of analog circuit design, such as circuit topo
Externí odkaz:
http://arxiv.org/abs/2407.19150
The Hierarchy Of Time-Surfaces (HOTS) algorithm, a neuromorphic approach for feature extraction from event data, presents promising capabilities but faces challenges in accuracy and compatibility with neuromorphic hardware. In this paper, we introduc
Externí odkaz:
http://arxiv.org/abs/2404.12402
As the use of neuromorphic, event-based vision sensors expands, the need for compression of their output streams has increased. While their operational principle ensures event streams are spatially sparse, the high temporal resolution of the sensors
Externí odkaz:
http://arxiv.org/abs/2403.08086
Dimensionality reduction is crucial for controlling nonlinear partial differential equations (PDE) through a "reduce-then-design" strategy, which identifies a reduced-order model and then implements model-based control solutions. However, inaccuracie
Externí odkaz:
http://arxiv.org/abs/2403.01005
Spatiotemporal modeling is critical for understanding complex systems across various scientific and engineering disciplines, but governing equations are often not fully known or computationally intractable due to inherent system complexity. Data-driv
Externí odkaz:
http://arxiv.org/abs/2402.15636
Selective attention is an essential mechanism to filter sensory input and to select only its most important components, allowing the capacity-limited cognitive structures of the brain to process them in detail. The saliency map model, originally deve
Externí odkaz:
http://arxiv.org/abs/2401.05030
Autor:
Mowlavi, Saviz, Benosman, Mouhacine
Designing estimation algorithms for systems governed by partial differential equations (PDEs) such as fluid flows is challenging due to the high-dimensional and oftentimes nonlinear nature of the dynamics, as well as their dependence on unobserved ph
Externí odkaz:
http://arxiv.org/abs/2312.11839
We introduce controlgym, a library of thirty-six industrial control settings, and ten infinite-dimensional partial differential equation (PDE)-based control problems. Integrated within the OpenAI Gym/Gymnasium (Gym) framework, controlgym allows direc
Externí odkaz:
http://arxiv.org/abs/2311.18736
We introduce the receding-horizon policy gradient (RHPG) algorithm, the first PG algorithm with provable global convergence in learning the optimal linear estimator designs, i.e., the Kalman filter (KF). Notably, the RHPG algorithm does not require a
Externí odkaz:
http://arxiv.org/abs/2309.04831
Autor:
Saviz Mowlavi, Mouhacine Benosman
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract State estimators such as Kalman filters compute an estimate of the instantaneous state of a dynamical system from sparse sensor measurements. For spatio-temporal systems, whose dynamics are governed by partial differential equations (PDEs),
Externí odkaz:
https://doaj.org/article/34e156cf2b8d4db181f851b9aeb04954
