Compositional Uncertainty Analysis via Importance Weighted Gibbs Sampling for Coupled Multidisciplinary Systems

Ghoreishi, Seyede Fatemeh; Allaire, Douglas

doi:10.2514/6.2016-1443

Cited by 17 publications

(10 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…When the number of states is large, the exact computation of the BKF and the BKS becomes intractable, due to the large size of the matrices involved, which each contain 2 2d elements, and approximate methods must be used, such as sequential Monte-Carlo methods, also known as particle filter algorithms which have been successfully applied in various fields [26][27][28][29][30]. In the next subsections, we describe particle filter implementations of the BKF and BKS.…”

Section: Particle Filters For State Estimationmentioning

confidence: 99%

Maximum-Likelihood Adaptive Filter for Partially Observed Boolean Dynamical Systems

Imani

Braga-Neto

2017

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

Partially-observed Boolean dynamical systems (POBDS) are a general class of nonlinear models with application in estimation and control of Boolean processes based on noisy and incomplete measurements. The optimal minimum mean square error (MMSE) algorithms for POBDS state estimation, namely, the Boolean Kalman filter (BKF) and Boolean Kalman smoother (BKS), are intractable in the case of large systems, due to computational and memory requirements. To address this, we propose approximate MMSE filtering and smoothing algorithms based on the auxiliary particle filter (APF) method from sequential Monte-Carlo theory. These algorithms are used jointly with maximum-likelihood (ML) methods for simultaneous state and parameter estimation in POBDS models. In the presence of continuous parameters, ML estimation is performed using the expectation-maximization (EM) algorithm; we develop for this purpose a special smoother which reduces the computational complexity of the EM algorithm. The resulting particle-based adaptive filter is applied to a POBDS model of Boolean gene regulatory networks observed through noisy RNA-Seq time series data, and performance is assessed through a series of numerical experiments using the well-known cell cycle gene regulatory model.

show abstract

Section: Particle Filters For State Estimationmentioning

confidence: 99%

Maximum-Likelihood Adaptive Filter for Partially Observed Boolean Dynamical Systems

Imani

Braga-Neto

2017

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

show abstract

“…The parameters σ 2 f and l are to be determined. It is worth mentioning that factorization in equation (22) depends on the fact that the multiplication of two separate kernels results in another kernel [33]- [35].…”

Section: Control Using Reinforcement Learning and Gaussian Procesmentioning

confidence: 99%

Control of Gene Regulatory Networks With Noisy Measurements and Uncertain Inputs

Imani

Braga-Neto

2018

IEEE Trans. Control Netw. Syst.

View full text Add to dashboard Cite

Abstract-This paper is concerned with the problem of stochastic control of gene regulatory networks (GRNs) observed indirectly through noisy measurements and with uncertainty in the intervention inputs. The partial observability of the gene states and uncertainty in the intervention process are accounted for by modeling GRNs using the partially-observed Boolean dynamical system (POBDS) signal model with noisy gene expression measurements. Obtaining the optimal infinite-horizon control strategy for this problem is not attainable in general, and we apply reinforcement learning and Gaussian process techniques to find a near-optimal solution. The POBDS is first transformed to a directly-observed Markov Decision Process in a continuous belief space, and the Gaussian process is used for modeling the cost function over the belief and intervention spaces. Reinforcement learning then is used to learn the cost function from the available gene expression data. In addition, we employ sparsification, which enables the control of large partially-observed GRNs. The performance of the resulting algorithm is studied through a comprehensive set of numerical experiments using synthetic gene expression data generated from a melanoma gene regulatory network.

show abstract

“…Let assume we have M information sources available. Each information source describes the quantity of interest, f (x), at design point x with the associated fidelity represented by additive independent identically distributed Gaussian noise as [39][40][41]:…”

Section: A Fused Gaussian Processmentioning

confidence: 99%

A Fusion-Based Multi-Information Source Optimization Approach using Knowledge Gradient Policies

Ghoreishi

Allaire

2018

2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

Self Cite

View full text Add to dashboard Cite

Optimization of complex systems often involves evaluation of a quantity several times, which is potentially computationally prohibitive. This can be alleviated by considering information sources representing the original model with lower fidelity and cost. This paper describes an optimization method for the case where the objective function is represented by different information sources with varying fidelities and computational costs. The proposed methodology creates a multi-information source value-of-information framework that defines optimal strategies for querying of information sources. The surrogate Gaussian process model is used for fusion of information sources. Then, the knowledge gradient policy is incorporated by considering the probability of violation of constraints for sequential decision making to identify the next design and information source to evaluate. The high performance of the developed methodology is demonstrated in terms of making balance between cost and information gain of various information sources of a one-dimensional example test problem and an aerodynamic design example.

show abstract

Compositional Uncertainty Analysis via Importance Weighted Gibbs Sampling for Coupled Multidisciplinary Systems

Cited by 17 publications

References 38 publications

Maximum-Likelihood Adaptive Filter for Partially Observed Boolean Dynamical Systems

Maximum-Likelihood Adaptive Filter for Partially Observed Boolean Dynamical Systems

Control of Gene Regulatory Networks With Noisy Measurements and Uncertain Inputs

A Fusion-Based Multi-Information Source Optimization Approach using Knowledge Gradient Policies

Contact Info

Product

Resources

About