The stochastic nature of turbulent two-phase flow determines that the deterministic modeling approaches always have limited predicting range and accuracy, which is due to the averaging and approximation made during the model developments. On the other hand, well-developed machine learning models...

We examine how the return predictability of deep learning models varies with stocks’ vulnerability to investors’ behavioral biases. Using an extensive list of anomaly variables, we find that the long-short strategy based on deep learning signals generates greater returns for stocks that are...

A novel multi-condition multi-objective optimization method that can find Pareto front over a defined condition space is developed using deep reinforcement learning. Unlike the conventional methods which perform optimization at a single condition, the present method learns correlations between...

ABSTRACT. Models of physical systems often have elusive constitutive details which must be determined empirically. A new method, approximate model inference (AMI), is proposed for data-driven parameter estimation in differential equations where uncertainties exist in the model itself. This is...

Modelling and forecasting electricity consumption (EC) help industry managers make better strategic decisions. In this study, a hybrid approach for predicting EC is proposed which first EC is decomposed into approximate and detail parts based on wavelet transfer (WT). Next, the modeling of...

Cavitation is a phenomenon that reduces the useful life of hydraulic machines, taking place in function of the variation of the pressure gradient at a constant temperature. In turbines, cavitation occurs when the turbine operates beyond nominal conditions, generating abnormal vibrations, erosion...

Today’s dynamic world demands the design and deployment of embedded systems and System-n-Chip design in various applications including both domestic and industrial areas, especially in the establishment of a smart and sustainable society. This necessitates the inclusion of hardware-based...

We survey recent methodological contributions in asset pricing using factor models and machine learning. We organize these results based on their primary objectives: estimating expected returns, factors, risk exposures, risk premia, and the stochastic discount factor, as well as model comparison...

We investigate the relationship between accumulated experience completing wind power projects and the cost of installing wind projects in the U.S. from 2001-2015. Our modeling framework disentangles accumulated experience from input price changes, scale economies, and exogenous technical change;...