Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Mowlavi, Saviz"'
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
Autor:
Schperberg, Alexander, Tanaka, Yusuke, Mowlavi, Saviz, Xu, Feng, Balaji, Bharathan, Hong, Dennis
State estimation for legged robots is challenging due to their highly dynamic motion and limitations imposed by sensor accuracy. By integrating Kalman filtering, optimization, and learning-based modalities, we propose a hybrid solution that combines
Externí odkaz:
http://arxiv.org/abs/2401.16719
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:
Mowlavi, Saviz, Kamrin, Ken
Detecting hidden geometrical structures from surface measurements under electromagnetic, acoustic, or mechanical loading is the goal of noninvasive imaging techniques in medical and industrial applications. Solving the inverse problem can be challeng
Externí odkaz:
http://arxiv.org/abs/2303.09280
Autor:
Mowlavi, Saviz, Benosman, Mouhacine
In systems governed by nonlinear partial differential equations such as fluid flows, the design of state estimators such as Kalman filters relies on a reduced-order model (ROM) that projects the original high-dimensional dynamics onto a computational
Externí odkaz:
http://arxiv.org/abs/2302.01189
Autor:
Mowlavi, Saviz1 (AUTHOR) mowlavi@merl.com, Benosman, Mouhacine1 (AUTHOR) benosman@merl.com
Publikováno v:
Scientific Reports. 9/28/2024, Vol. 14 Issue 1, p1-13. 13p.
Autor:
Mowlavi, Saviz, Nabi, Saleh
Physics-informed neural networks (PINNs) have recently become a popular method for solving forward and inverse problems governed by partial differential equations (PDEs). By incorporating the residual of the PDE into the loss function of a neural net
Externí odkaz:
http://arxiv.org/abs/2111.09880