HilbertNet: A Probabilistic Machine Learning Framework for Frequency Response Extrapolation of Electromagnetic Structures

2021 ◽  
pp. 1-13
Author(s):  
Osama Waqar Bhatti ◽  
Hakki Mert Torun ◽  
Madhavan Swaminathan
Keyword(s):  
Machine Learning ◽  
Frequency Response ◽  
Learning Framework ◽  
Probabilistic Machine Learning

scholarly journals Reliable Evapotranspiration Predictions with a Probabilistic Machine Learning Framework

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 557
Author(s):  
Hakan Başağaoğlu ◽  
Debaditya Chakraborty ◽  
James Winterle
Keyword(s):  
Machine Learning ◽  
Arid Regions ◽  
Probabilistic Approach ◽  
Water Evaporation ◽  
Reference Crop Evapotranspiration ◽  
Learning Framework ◽  
Groundwater Irrigation ◽  
Probabilistic Machine Learning ◽  
Hydroclimatic Variables ◽  
Reference Crop

Evapotranspiration is often expressed in terms of reference crop evapotranspiration (ETo), actual evapotranspiration (ETa), or surface water evaporation (Esw), and their reliable predictions are critical for groundwater, irrigation, and aquatic ecosystem management in semi-arid regions. We demonstrated that a newly developed probabilistic machine learning (ML) model, using a hybridized “boosting” framework, can simultaneously predict the daily ETo, Esw, & ETa from local hydroclimate data with high accuracy. The probabilistic approach exhibited great potential to overcome data uncertainties, in which 100% of the ETo, 89.9% of the Esw, and 93% of the ETa test data at three watersheds were within the models’ 95% prediction intervals. The modeling results revealed that the hybrid boosting framework can be used as a reliable computational tool to predict ETo while bypassing net solar radiation calculations, estimate Esw while overcoming uncertainties associated with pan evaporation & pan coefficients, and predict ETa while offsetting high capital & operational costs of EC towers. In addition, using the Shapley analysis built on a coalition game theory, we identified the order of importance and interactions between the hydroclimatic variables to enhance the models’ transparency and trustworthiness.

A Probabilistic Machine Learning Framework for Explicit Inverse Design of Industrial Gas Turbine Blades

2021 ◽  
Author(s):  
Sayan Ghosh ◽  
Valeria Andreoli ◽  
Govinda A. Padmanabha ◽  
Cheng Peng ◽  
Steven Atkinson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gas Turbines ◽  
Turbine Blades ◽  
Inverse Design ◽  
Learning Framework ◽  
Industrial Gas Turbines ◽  
Critical Components ◽  
Turbine Blade Design ◽  
The Individual ◽  
Probabilistic Machine Learning

Abstract One of the critical components in Industrial Gas Turbines (IGT) is the turbine blade. Design of turbine blades needs to consider multiple aspects like aerodynamic efficiency, durability, safety and manufacturing, which make the design process sequential and iterative. The sequential nature of these iterations forces a long design cycle time, ranging from a several months to years. Due to the reactionary nature of these iterations, little effort has been made to accumulate data in a manner that allows for deep exploration and understanding of the total design space. This is exemplified in the process of designing the individual components of the IGT resulting in a potential unrealized efficiency. To overcome the aforementioned challenges, we demonstrate a probabilistic inverse design machine learning framework, namely Pro-ML IDeAS, to carry out an explicit inverse design. Pro-ML IDeAS calculates the design explicitly without costly iteration and overcomes the challenges associated with ill-posed inverse problems. In this work the framework will be demonstrated on inverse aerodynamic design of 2D airfoil of turbine blades.

scholarly journals SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Justin Y. Lee ◽  
Britney Nguyen ◽  
Carlos Orosco ◽  
Mark P. Styczynski
Keyword(s):  
Machine Learning ◽  
Metabolic Networks ◽  
Sampling Frequency ◽  
Low Noise ◽  
Training Data ◽  
High Noise ◽  
Regulatory Interactions ◽  
Learning Framework ◽  
Metabolic Systems ◽  
Noise Data

Abstract Background The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR. Results We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6–27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. Conclusions SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

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