Model-free Data-Driven inference in computational mechanics

Dynamic-Data-Driven Real-Time Computational Mechanics Environment

The proliferation of sensor networks in various areas of technology has enabled real-time behavioral monitoring of various physical systems in various length and time scales. The opportunity to use these data dynamically for improving speed, accuracy, and general performance of predictive behavior modeling simulation is of paramount importance. The present paper identifies enabling modeling met...

Model-Free Inference for ChIP-Seq Data

Due to its higher resolution mapping and stronger ChIP enrichment signals, ChIP-seq tends to replace ChIP-chip technology in studying genome-wide protein-DNA interactions, while the massive digital ChIP-seq data present new challenges to statisticians. To date, most methods proposed in the literature for ChIP-seq data analysis are model based, however, finding a single model workable for all da...

Network Model-Assisted Inference from Respondent-Driven Sampling Data.

Respondent-Driven Sampling is a widely-used method for sampling hard-to-reach human populations by link-tracing over their social networks. Inference from such data requires specialized techniques because the sampling process is both partially beyond the control of the researcher, and partially implicitly defined. Therefore, it is not generally possible to directly compute the sampling weights ...

Data-driven Building Metadata Inference

Data Driven Computational Model for Bipedal Walking and Push Recovery

In this research, we have developed the data driven computational walking model to overcome the problem with traditional kinematics based model. Our model is adaptable and can adjust the parameter morphological similar to human. The human walk is a combination of different discrete sub-phases with their continuous dynamics. Any system which exhibits the discrete switching logic and continuous d...