scholarly journals Data-driven prediction of multistable systems from sparse measurements

2021 ◽  
Vol 31 (6) ◽  
pp. 063118
Author(s):  
Bryan Chu ◽  
Mohammad Farazmand
Keyword(s):  
Data Driven ◽  
Sparse Measurements ◽  
Multistable Systems

Nonparametric and data-driven interpolation of subsurface soil stratigraphy from limited data using multiple point statistics

2020 ◽  
pp. 1-20
Author(s):  
Chao Shi ◽  
Yu Wang
Keyword(s):  
Spatial Information ◽  
Real Data ◽  
Linear Interpolation ◽  
Supervised Machine Learning ◽  
Data Driven ◽  
Multiple Point ◽  
Usual Practice ◽  
Multiple Point Statistics ◽  
Subsurface Soil ◽  
Sparse Measurements

An essential task in many geotechnical projects is delineation of subsurface soil stratigraphy from scatter measurements. Geotechnical engineers often use their knowledge on local geology and interpret soil strata boundaries by linear interpolation of measured data. This usual practice may encounter difficulties when interpreting complex deposits, particularly when measurements are limited. In this study, a novel nonparametric, data-driven method based on multiple point statistics (MPS) is proposed to interpolate subsurface soil stratigraphy from sparse measurements. MPS may be formulated as Bayesian supervised machine learning, which adaptively learns high-order spatial information (e.g., curvilinear features of soil layers) using sparse measurements obtained in a specific site and training image that reflects pre-existing engineering knowledge on similar geological settings. The proposed method is the first ever purely data-driven method (i.e., without using any pre-specified parametric functions) for geotechnical site characterization. The proposed method is illustrated by a simulated example and real data from a reclamation site in Hong Kong. The proposed method not only accurately interpolates the subsurface soil stratigraphy from sparse measurements, but also quantifies uncertainty associated with the interpolation. Effects of governing parameters in the proposed method are explicitly investigated, and parameters appropriate for subsurface soil stratigraphy are identified.

scholarly journals Decoding mouse behavior to explain single-trial decisions and their relationship with neural activity

2019 ◽  
Author(s):  
Yves Weissenberger ◽  
Andrew J. King ◽  
Johannes C. Dahmen
Keyword(s):  
Neural Activity ◽  
Operant Behavior ◽  
Motor Output ◽  
Data Driven ◽  
Cognitive Variables ◽  
Single Trial ◽  
Mouse Behavior ◽  
Sparse Measurements ◽  
Long Timescales ◽  
The Relationship

AbstractModels of behavior typically focus on sparse measurements of motor output over long timescales, limiting their ability to explain momentary decisions or neural activity. We developed data-driven models relating experimental variables to videos of behavior. Applied to mouse operant behavior, they revealed behavioral encoding of cognitive variables. Model-based decoding of videos yielded an accurate account of single-trial behavior in terms of the relationship between cognition, motor output and cortical activity.

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