Sparse Data Driven Mesh Deformation

AbstractHow do we forecast an emerging pandemic in real time in a purely data-driven manner? How to leverage rich heterogeneous data based on various signals such as mobility, testing, and/or disease exposure for forecasting? How to handle noisy data and generate uncertainties in the forecast? In this paper, we present DeepCovid, an operational deep learning framework designed for real-time COVID-19 forecasting. Deep-Covid works well with sparse data and can handle noisy heterogeneous data signals by propagating the uncertainty from the data in a principled manner resulting in meaningful uncertainties in the forecast. The deployed framework also consists of modules for both real-time and retrospective exploratory analysis to enable interpretation of the forecasts. Results from real-time predictions (featured on the CDC website and FiveThirtyEight.com) since April 2020 indicates that our approach is competitive among the methods in the COVID-19 Forecast Hub, especially for short-term predictions.

Download Full-text

Data-driven weight optimization for real-time mesh deformation

Graphical Models ◽

10.1016/j.gmod.2019.101037 ◽

2019 ◽

Vol 104 ◽

pp. 101037

Author(s):

Yu-Jie Yuan ◽

Yu-Kun Lai ◽

Tong Wu ◽

Shihong Xia ◽

Lin Gao

Keyword(s):

Real Time ◽

Data Driven ◽

Mesh Deformation ◽

Weight Optimization

Download Full-text

On Data-Driven Sparse Sensing and Linear Estimation of Fluid Flows

Sensors ◽

10.3390/s20133752 ◽

2020 ◽

Vol 20 (13) ◽

pp. 3752

Author(s):

Balaji Jayaraman ◽

S M Abdullah Al Mamun

Keyword(s):

Fluid Flows ◽

Sparse Data ◽

Data Driven ◽

Estimation Methods ◽

Natural Environments ◽

Fine Scale ◽

Linear Estimation ◽

Performance Improvements ◽

Learning Machine ◽

Low Dimensional

The reconstruction of fine-scale information from sparse data measured at irregular locations is often needed in many diverse applications, including numerous instances of practical fluid dynamics observed in natural environments. This need is driven by tasks such as data assimilation or the recovery of fine-scale knowledge including models from limited data. Sparse reconstruction is inherently badly represented when formulated as a linear estimation problem. Therefore, the most successful linear estimation approaches are better represented by recovering the full state on an encoded low-dimensional basis that effectively spans the data. Commonly used low-dimensional spaces include those characterized by orthogonal Fourier and data-driven proper orthogonal decomposition (POD) modes. This article deals with the use of linear estimation methods when one encounters a non-orthogonal basis. As a representative thought example, we focus on linear estimation using a basis from shallow extreme learning machine (ELM) autoencoder networks that are easy to learn but non-orthogonal and which certainly do not parsimoniously represent the data, thus requiring numerous sensors for effective reconstruction. In this paper, we present an efficient and robust framework for sparse data-driven sensor placement and the consequent recovery of the higher-resolution field of basis vectors. The performance improvements are illustrated through examples of fluid flows with varying complexity and benchmarked against well-known POD-based sparse recovery methods.

Download Full-text