Search Results for author:
Found 9 papers, 3 papers with code
Policy Optimization for PDE Control with a Warm Start
The PO step fine-tunes the model-based controller to compensate for the modeling error from dimensionality reduction.
Smooth and Sparse Latent Dynamics in Operator Learning with Jerk Regularization
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.
OptiState: State Estimation of Legged Robots using Gated Networks with Transformer-based Vision and Kalman Filtering
State estimation for legged robots is challenging due to their highly dynamic motion and limitations imposed by sensor accuracy.
Dual parametric and state estimation for partial differential equations
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 physical parameters.
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning Algorithms
This project serves the learning for dynamics & control (L4DC) community, aiming to explore key questions: the convergence of RL algorithms in learning control policies; the stability and robustness issues of learning-based controllers; and the scalability of RL algorithms to high- and potentially infinite-dimensional systems.
Global Convergence of Receding-Horizon Policy Search in Learning Estimator Designs
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).
Topology optimization with physics-informed neural networks: application to noninvasive detection of hidden geometries
We validate our framework by detecting the number, locations, and shapes of hidden voids and inclusions in linear and nonlinear elastic bodies using measurements of outer surface displacement from a single mechanical loading experiment.
Reinforcement learning-based estimation for partial differential equations
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 computationally tractable low-dimensional space.
Reinforcement Learning State Estimation for High-Dimensional Nonlinear Systems
In high-dimensional nonlinear systems such as fluid flows, the design of state estimators such as Kalman filters relies on a reduced-order model (ROM) of the dynamics.
