Scientific Machine Learning for Coarse-Grained Constitutive Models
نویسندگان
چکیده
منابع مشابه
Distributed Machine Learning: Scaling Up with Coarse-grained Parallelism
Machine learning methods are becoming accepted as additions to the biologists data-analysis tool kit. However, scaling these techniques up to large data sets, such as those in biological and medical domains, is problematic in terms of both the required computational search effort and required memory (and the detrimental effects of excessive swapping). Our approach to tackling the problem of sca...
متن کاملCoarse-grained models for protein aggregation.
The aggregation of soluble proteins into fibrillar species is a complex process that spans many lengths and time scales, and that involves the formation of numerous on-pathway and off-pathway intermediate species. Despite this complexity, several elements underlying the aggregation process appear to be universal. The kinetics typically follows a nucleation-growth process, and proteins with very...
متن کاملCoarse-grained models for macromolecular systems
Neutron scattering experiments and simulations are often used as complementary tools in view of revealing the structure and dynamics of molecular and macromolecular systems. For polymeric and selfassembling systems, the simulation of large-scale structures and long-time processes is often achieved by using coarse-grained models which allow to gain some orders of magnitude in space and time scal...
متن کاملSimplified Models for Coarse-Grained Hemodynamics Simulations
Human blood can be approximated as a dense suspension of red blood cells in plasma. Here, we present two models we recently developed to investigate blood flow on different scales: in the first part of the paper we concentrate on describing individual cells or model systems such as vesicles with high resolution in order to understand the underlying fundamental properties of bulk hemodynamics. H...
متن کاملMachine Learning Models for Housing Prices Forecasting using Registration Data
This article has been compiled to identify the best model of housing price forecasting using machine learning methods with maximum accuracy and minimum error. Five important machine learning algorithms are used to predict housing prices, including Nearest Neighbor Regression Algorithm (KNNR), Support Vector Regression Algorithm (SVR), Random Forest Regression Algorithm (RFR), Extreme Gradient B...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Procedia Manufacturing
سال: 2020
ISSN: 2351-9789
DOI: 10.1016/j.promfg.2020.04.211