The generation of simulation data from physical models trying to mimic parts of the real-world process as accurately as possible has received much attention in industry during the last years. A proper augmentation of simulation data with (available or recordable) real-measured data is an intrinsic ...
Accurate determination of fuel properties of complex mixtures over a wide range of pressure and temperature conditions is essential to utilizing alternative fuels. The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical p...
In conducting and teaching Building Simulation, we oftenrnfind two main disadvantages of conventional models: inconsistencyrnof simulation results obtained by different users of the same model,rnand long machine times required for annual simulations of relativelyrnsimple buildings.rnIn searching for ...
The machine-learning-driven deposition models presented in this study may open up opportunities for investigating multi-element metallic or alloy substrates in the growth of diverse carbon nanostructures, such as graphene, CNTs, graphite- or diamond-like carbon films. Methods Hybrid molecular dynamics ...
Simulation software models real-world environments. When planning a manufacturing project—from an entire workflow down to a single part—simulation software contains mathematical values and calculations that represent the impact and behavior of real-world forces. ...
Simulation software models real-world environments. When planning a manufacturing project—from an entire workflow down to a single part—simulation software contains mathematical values and calculations that represent the impact and behavior of real-world forces. ...
Heterogeneous catalysis is at the heart of chemistry. New theoretical methods based on machine learning (ML) techniques that emerged in recent years provide a new avenue to disclose the structures and reaction in complex catalytic systems. Here we review
discoal is a coalescent simulation program capable of simulating models with recombination, selective sweeps, and demographic changes including population splits, admixture events, and ancient samples recombination simulation-model admixture-events selective-sweeps demographic-changes Updated Jun 1, 2022 TeX...
2.1. Machine Learning Methods for Surrogate Models The first attempt to develop a fast proxy of a subsurface reservoir simulator was accomplished by Mohaghegh and his associates, who ran numerous studies and set up SRM methodologies by utilizing intelligent system techniques to approximate the simulatio...
The Xcelium SimAI App harnesses the power of machine learning technology. It builds models from regressions run in the Xcelium simulator, enabling the generation of new regressions with specific targets. This includes efficient soak testing of the entire design or specific areas and improved regression...