Gaussian Process Regression and Monte Carlo Simulation to Determine VOC Biomarker Concentrations Via Chemiresistive Gas Nanosensors

Rivera, Paula Angarita; Woollam, Mark; Siegel, Amanda P.; Agarwal, Mangilal

doi:10.1109/dts52014.2021.9498219

Cited by 6 publications

(1 citation statement)

References 24 publications

(8 reference statements)

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“…An intelligent selection of the mean and the covariance functions can lead to powerful regression models that capture prior knowledge about 𝑓 𝐱 such as global trends and smoothness. A common practice is to build GP regression models with a zero-mean function 𝜇 𝐱 0 due to the descriptive power of 𝑘 𝐱, 𝐱 [31,32]. Valid covariance functions produce positive semi-definite matrices regardless of the chosen pair of points 𝐱, 𝐱 [15,31].…”

Section: Gaussian Process Regressionmentioning

confidence: 99%

Multi-Objective Bayesian Optimization of Lithium-Ion Battery Cells for Electric Vehicle Operational Scenarios

Gaonkar

Valladares

Tovar

et al. 2022

Electronic Materials

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The development of lithium-ion batteries (LIBs) based on current practice allows an energy density increase estimated at 10% per year. However, the required power for portable electronic devices is predicted to increase at a much faster rate, namely 20% per year. Similarly, the global electric vehicle battery capacity is expected to increase from around 170 GWh per year today to 1.5 TWh per year in 2030—this is an increase of 125% per year. Without a breakthrough in battery design technology, it will be difficult to keep up with their increasing energy demand. The objective of this investigation is to develop a design methodology to accelerate the LIB development through the integration of electro-chemical numerical simulations and machine learning algorithms. In this work, the Gaussian process (GP) regression model is used as a fast approximation of numerical simulation (conducted using Simcenter Battery Design Studio®). The GP regression models are systematically updated through a multi-objective Bayesian optimization algorithm, which enables the exploration of innovative designs as well as the determination of optimal configurations. The results reported in this work include optimal thickness and porosities of LIB electrodes for several practical charge–discharge scenarios which maximize energy density and minimize capacity fade.

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Section: Gaussian Process Regressionmentioning

confidence: 99%

Multi-Objective Bayesian Optimization of Lithium-Ion Battery Cells for Electric Vehicle Operational Scenarios

Gaonkar

Valladares

Tovar

et al. 2022

Electronic Materials

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Multi-Objective Bayesian Optimization of Lithium-Ion Battery Cells

Gaonkar¹,

Valladares²,

El-Mounayri³

et al. 2022

SAE Technical Paper Series

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Nonlinear Multi-Fidelity Bayesian Optimization: An Application in the Design of Blast Mitigating Structures

Valladares¹,

Tovar²

2022

SAE Int. J. Adv. & Curr. Prac. in Mobility

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<div class="section abstract"><div class="htmlview paragraph">A common scenario in engineering design is the availability of several black-box functions that describe an event with different levels of accuracy and evaluation cost. Solely employing the highest fidelity, often the most expensive, black-box function leads to lengthy and costly design cycles. Multi-fidelity modeling improves the efficiency of the design cycle by combining information from a small set of observations of the high-fidelity function and large sets of observations of the low-fidelity, fast-to-evaluate functions. In the context of Bayesian optimization, the most popular multi-fidelity model is the auto-regressive (AR) model, also known as the co-kriging surrogate. The main building block of the AR model is a weighted sum of two Gaussian processes (GPs). Therefore, the AR model is well suited to exploit information generated by sources that present strong linear correlations. Recently, the non-linear auto-regressive Gaussian process (NARGP) model has appeared as an alternative to integrate information generated by non-linearly correlated black-box functions. The performance of the NARGP model in structural optimization has remained largely unexplored. This investigation presents a Bayesian optimization approach that implements the NARGP model as the multi-fidelity surrogate model. The optimization strategy is utilized in the design sandwich composite armors for blast mitigation. The armors are made of four layers: steel, carbon fiber reinforced polymer (CFRP), aluminum honeycomb (HC), and CFRP. The optimization problem has three design variables, which are the thickness of the CFRP and aluminum HC layers. Two objectives are minimized: the armor’s penetrations and the reaction force at the armor’s supports. The black-box functions are two finite element models with different levels of fidelity. The low-fidelity model assumes elastic behavior of the sandwich composite. The high-fidelity model considers the nonlinear behavior of each layer of the armor. The results show that the proposed non-linear multi-fidelity Bayesian optimization approach produces a more stable expansion of the Pareto front than an optimization strategy that employs the AR model. This outcome suggests that the NARGP model is an appealing alternative in design problems with a limited number of function evaluations of the high-fidelity source.</div></div>

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Gaussian Process Regression and Monte Carlo Simulation to Determine VOC Biomarker Concentrations Via Chemiresistive Gas Nanosensors

Cited by 6 publications

References 24 publications

Multi-Objective Bayesian Optimization of Lithium-Ion Battery Cells for Electric Vehicle Operational Scenarios

Multi-Objective Bayesian Optimization of Lithium-Ion Battery Cells for Electric Vehicle Operational Scenarios

Multi-Objective Bayesian Optimization of Lithium-Ion Battery Cells

Nonlinear Multi-Fidelity Bayesian Optimization: An Application in the Design of Blast Mitigating Structures

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