machine learningwater distribution systemsurban drainage systemsartificial neural networksSurrogate models replace computationally expensive simulations of physically‐based models to obtain accurate results at a fraction of the time. These surrogate models, also known as metamodels, have been employed for ...
Some of the simple surrogate models used to understand opaque machine learning (ML) models, such as rule lists and sparse decision trees, bear some resemblance to scientific toy models. They allow non-experts to understand how an opaque ML model works globally via a much simpler model that ...
A surrogate machine-learning model replaces ab initio simulations by mapping a crystal structure to properties such as formation enthalpy, elastic constants, or band gaps, etc. Its utility lies in the fact that once the model is trained, properties of new materials can be predicted very quickly...
The proposed model is trained and tested for the case study of the city of Zurich, in Switzerland, and is compared with one of the most advanced models for building retrofit that uses building simulation and optimization tools. The surrogate model operates on a smaller input set and the time...
In this paper, we present a computationally inexpensive, accurate, data-driven surrogate model that directly learns the microstructural evolution of targeted systems by combining phase-field and history-dependent machine-learning techniques. We integrate a statistically representative, low-dimensional ...
Surrogate model of turbulent transport in fusion plasmas using machine learning The advent of machine learning (ML) has revolutionized the research of plasma confinement, offering new avenues for exploration. It enables the constructio... H Li,L Wang,YL Fu,... - IOP Publishing Ltd 被引量: 0...
Using data from Norfolk rainfall events between 2016 and 2018, this study compares the performance of a previous surrogate model based on a random forest algorithm with two deep learning models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The comparison of deep learning to ...
The proxy model is a mathematical model used to simulate the behaviour ofplex systems. It is designed to simplifyplex systems and make them easier to process. Proxy models are usually built on existing data or empirical knowledge, using mathematics, statistics or machine learning. It can replace...
Construction of a surrogate model is comprised of three steps: (1) selection of the sample points, (2) optimization or “training” of the model parameters, and (3) evaluation of the accuracy of the surrogate model (Wang et al., 2014). Although several machine learning and regression techni...
,n}, the linear regression model is expressed as: y=β0+∑i=1n(βixi+ϵi) (2) where y is the vector of dependent variables (i.e., observed response), β is the coefficients vector, β0 is the intercept (or bias in machine learning), and ϵi is the random error term. ...