Dual parametric and state estimation for partial differential equations
원문 초록
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. In this paper, we propose two different lightweight and effective methodologies for real-time state estimation of PDEs in the presence of parametric uncertainties. Both approaches combine a Kalman filter with a data-driven polytopic linear reduced-order model obtained by dynamic mode decomposition (DMD). Using examples involving the nonlinear Burgers and Navier-Stokes equations, we demonstrate accurate estimation of both the state and the unknown physical parameter along system trajectories corresponding to various physical parameter values.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
From Models To Experiments: Shallow Recurrent Decoder Networks on the DYNASTY Experimental Facility
Recursive KalmanNet: Deep Learning-Augmented Kalman Filtering for State Estimation with Consistent Uncertainty Quantification
Benchmarking Spatiotemporal Reasoning in LLMs and Reasoning Models: Capabilities and Challenges
UMotion: Uncertainty-driven Human Motion Estimation from Inertial and Ultra-wideband Units
Contrastive Cross-Course Knowledge Tracing via Concept Graph Guided Knowledge Transfer
Secure Safety Filter Design for Sampled-data Nonlinear Systems under Sensor Spoofing Attacks
토론
이 논문과 연결된 포럼 글이 아직 없습니다.