Feature-Informed Data Assimilation -- Definitions and Illustrative Examples
We introduce a mathematical formulation of feature-informed data assimilation (FIDA). In FIDA, the information about feature events, such as shock waves, level curves, wavefronts and peak value, in dynamical systems are used for the estimation of state variables and unknown parameters. The observation operator in FIDA is a set-valued functional, which is fundamentally different from the observation operators in conventional data assimilation. Demonstrated in three example, FIDA problems introduced in this note exist in a wide spectrum of applications in science and engineering.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Combined State and Parameter Estimation in Level-Set Methods
Reduced-order models based on level-set methods are widely used tools to qualitatively capture and track the nonlinear dynamics of an interface. The aim of this paper is to develop a physics-informed, data-driven, statis…
parameter estimationState EstimationUncertainty QuantificationADDA: a Modular Framework for Representing, Simulating and Assimilating Dynamics with End-to-end Differentiability
Data assimilation (DA) is an essential tool for prediction and understanding in the geosciences. DA combines simulation programs representing scientific knowledge with observations that constrain system dynamics, resulti…
Study of Drug Assimilation in Human System using Physics Informed Neural Networks
Differential equations play a pivotal role in modern world ranging from science, engineering, ecology, economics and finance where these can be used to model many physical systems and processes. In this paper, we study t…
Reinforcement learning and Bayesian data assimilation for model-informed precision dosing in oncology
Model-informed precision dosing (MIPD) using therapeutic drug/biomarker monitoring offers the opportunity to significantly improve the efficacy and safety of drug therapies. Current strategies comprise model-informed dos…
reinforcement-learningReinforcement Learning (RL)Bayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfires
We apply the Physics Informed Neural Network (PINN) to the problem of wildfire fire-front modelling. We use the PINN to solve the level-set equation, which is a partial differential equation that models a fire-front thro…
Uncertainty Quantification