arXiv subjects

Lo-Bin Chang

Publications and source records attributed to Lo-Bin Chang.

3 recordsLinked to original sources

arXiv2022

Modeling unknown dynamical systems with hidden parameters

We present a data-driven numerical approach for modeling unknown dynamical systems with missing/hidden parameters. The method is based on training a deep neural network (DNN) model for the unknown system using its trajectory data. A key feature is that the unknown dynamical system contains system parameters that are completely hidden, in the sense that no information about the parameters is available through either the measurement trajectory data or our prior knowledge of the system. We demonstrate that by training a DNN using the trajectory data with sufficient time history, the resulting DNN model can accurately model the unknown dynamical system. For new initial conditions associated with new, and unknown, system parameters, the DNN model can produce accurate system predictions over longer time.

stat.ML↗

arXiv2020

Learning reduced systems via deep neural networks with memory

We present a general numerical approach for constructing governing equations for unknown dynamical systems when only data on a subset of the state variables are available. The unknown equations for these observed variables are thus a reduced system of the complete set of state variables. Reduced systems possess memory integrals, based on the well known Mori-Zwanzig (MZ) formulism. Our numerical strategy to recover the reduced system starts by formulating a discrete approximation of the memory integral in the MZ formulation. The resulting unknown approximate MZ equations are of finite dimensional, in the sense that a finite number of past history data are involved. We then present a deep neural network structure that directly incorporates the history terms to produce memory in the network. The approach is suitable for any practical systems with finite memory length. We then use a set of numerical examples to demonstrate the effectiveness of our method.

stat.ML↗

arXiv2018

Association Analysis of Common and Rare SNVs using Adaptive Fisher Method to Detect Dense and Sparse Signals

The development of next generation sequencing (NGS) technology and genotype imputation methods enabled researchers to measure both common and rare variants in genome-wide association studies (GWAS). Statistical methods have been proposed to test a set of genomic variants together to detect if any of them is associated with the phenotype or disease. In practice, within the set of variants, there is an unknown proportion of variants truly causal or associated with the disease. Because most developed methods are sensitive to either the dense scenario, where a large proportion of the variants are associated, or the sparse scenario, where only a small proportion of the variants are associated, there is a demand of statistical methods with high power in both scenarios. In this paper, we propose a new association test (weighted Adaptive Fisher, wAF) that can adapt to both the dense and sparse scenario by adding weights to the Adaptive Fisher (AF) method we developed before. Using both simulation and the Genetic Analysis Workshop 16 (GAW16) data, we have shown that the new method enjoys comparable or better power to popular methods such as sequence kernel association test (SKAT and SKAT-O) and adaptive SPU (aSPU) test.

stat.ME↗