most citedMultiscale modeling of materials: Computing, data science,uncertainty and goal-oriented optimization

1 citations · 2 across the 2 of their papers we have counts for

collaborators

5 papers

cond-mat.mtrl-sci20211 cited

Concurrent goal-oriented materials-by-design

Xingsheng Sun, Burigede Liu, Kaushik Bhattacharya +1

The development of new materials and structures for extreme conditions including impact remains a continuing challenge despite steady advances. Design is currently accomplished usi…

cond-mat.mtrl-sci20211 cited

Multiscale modeling of materials: Computing, data science,uncertainty and goal-oriented optimization

Nikola Kovachki, Burigede Liu, Xingsheng Sun +4

The recent decades have seen various attempts at accelerating the process of developing materials targeted towards specific applications. The performance required for a particular…

physics.comp-ph2021

Hierarchical multiscale quantification of material uncertainty

Burigede Liu, Xingsheng Sun, Kaushik Bhattacharya +1

The macroscopic behavior of many materials is complex and the end result of mechanisms that operate across a broad range of disparate scales. An imperfect knowledge of material beh…

cs.LG2020

Multipole Graph Neural Operator for Parametric Partial Differential Equations

Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli +4

One of the main challenges in using deep learning-based methods for simulating physical systems and solving partial differential equations (PDEs) is formulating physics-based data…

cs.LG2020

Neural Operator: Graph Kernel Network for Partial Differential Equations

Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli +4

The classical development of neural networks has been primarily for mappings between a finite-dimensional Euclidean space and a set of classes, or between two finite-dimensional Eu…