Bayesian machine learning approach to the quantification of uncertainties on ab initio potential energy surfaces
This work introduces a novel methodology for the quantification of uncertainties associated
with potential energy surfaces (PESs) computed from first-principles quantum mechanical …
with potential energy surfaces (PESs) computed from first-principles quantum mechanical …
[HTML][HTML] Effective design space exploration of gradient nanostructured materials using active learning based surrogate models
Inspired by gradient structures in the nature, Gradient Nanostructured (GNS) metals have
emerged as a new class of materials with tunable microstructures. GNS metals can exhibit …
emerged as a new class of materials with tunable microstructures. GNS metals can exhibit …
Interdisciplinary research on designing engineering material systems: results from a national science foundation workshop
R Arroyave, S Shields… - Journal of …, 2018 - asmedigitalcollection.asme.org
We present the results from a workshop on interdisciplinary research on design of
engineering material systems, sponsored by the National Science Foundation. The …
engineering material systems, sponsored by the National Science Foundation. The …
Uncertainty quantification of artificial neural network based machine learning potentials
Atomistic simulations play an important role in the material analysis and design by being
rooted in the accurate first principles methods that free from empirical parameters and …
rooted in the accurate first principles methods that free from empirical parameters and …
A comparison of numerical optimizers in developing high dimensional surrogate models
Abstract The Gaussian Process (GP) model has become one of the most popular methods to
develop computationally efficient surrogate models in many engineering design …
develop computationally efficient surrogate models in many engineering design …
CVaR formulation of reliability-based design problems considering the risk of extreme failure events
SUMMARY & CONCLUSIONSThe reliability-based design optimization (RBDO) considers
uncertainties under probability constraints. The probability constraint ensures a minimum …
uncertainties under probability constraints. The probability constraint ensures a minimum …
Development of artificial neural network potential for graphene
Graphene exhibits a unique combination of mechanical, thermal and electrical properties
due to the strong and anisotropic bonding, enabling a wide range of novel thermal …
due to the strong and anisotropic bonding, enabling a wide range of novel thermal …
Machine learning and uncertainty quantification framework for predictive ab initio Hypersonics
S Venturi - 2021 - ideals.illinois.edu
Hypersonics represents one of the most challenging applications for predictive science. Due
to the multi-scale and multi-physics characteristics, high-Mach phenomena are generally …
to the multi-scale and multi-physics characteristics, high-Mach phenomena are generally …
Uncertainty Management and Reduction of Machine Learning Potential
View Video Presentation: https://doi. org/10.2514/6.2021-1962. vid Machine learning
potential has drawn more and more attention in the recent years due to its ability in enabling …
potential has drawn more and more attention in the recent years due to its ability in enabling …
[PDF][PDF] Interdisciplinary Research on Designing Engineering Material Systems: Results From a National Science Foundation Workshop
1.1 Emerging Importance of Materials Design. Recent advances across several fields have
afforded the opportunity to create advanced engineering material systems designed to have …
afforded the opportunity to create advanced engineering material systems designed to have …