Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems

Machine learning–driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins

(2022) Helgi I. Ingólfsson et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Analyses of internal structures and defects in materials using physics-informed neural networks

(2022) Enrui Zhang et al.   Science Advances

A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials

(2022) Somdatta Goswami et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Dynamic fracture of a bicontinuously nanostructured copolymer: A deep-learning analysis of big-data-generating experiment

(2022) Hanxun Jin et al.   JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS

Operator learning for predicting multiscale bubble growth dynamics

(2021) Chensen Lin et al.   JOURNAL OF CHEMICAL PHYSICS

Multiscale parareal algorithm for long-time mesoscopic simulations of microvascular blood flow in zebrafish

(2021) Ansel L. Blumers et al.   COMPUTATIONAL MECHANICS

DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks

(2021) Shengze Cai et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Mesoscopic and multiscale modelling in materials

(2021) Jacob Fish et al.   NATURE MATERIALS

A neural network-aided Bayesian identification framework for multiscale modeling of nanocomposites

(2021) Stefanos Pyrialakos et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A data-driven peridynamic continuum model for upscaling molecular dynamics

(2021) Huaiqian You et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

DeepM&Mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators

(2021) Zhiping Mao et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A seamless multiscale operator neural network for inferring bubble dynamics

(2021) Chensen Lin et al.   JOURNAL OF FLUID MECHANICS

A learning-based multiscale method and its application to inelastic impact problems

(2021) Burigede Liu et al.   JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS

Learning the solution operator of parametric partial differential equations with physics-informed DeepONets

(2021) Sifan Wang et al.   Science Advances

Multiscale Modeling Meets Machine Learning: What Can We Learn?

(2020) Grace C. Y. Peng et al.   ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING

Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms

(2020) A.T.M. Sakiur Rahman et al.   ADVANCES IN WATER RESOURCES

Machine learning of multiscale active force generation models for the efficient simulation of cardiac electromechanics

(2020) F. Regazzoni et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A robust solution of a statistical inverse problem in multiscale computational mechanics using an artificial neural network

(2020) Florent Pled et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Non-invasive inference of thrombus material properties with physics-informed neural networks

(2020) Minglang Yin et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Bayesian inference of non-linear multiscale model parameters accelerated by a Deep Neural Network

(2019) Ling Wu et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Modeling lamellar disruption within the aortic wall using a particle-based approach

(2019) H. Ahmadzadeh et al.   Scientific Reports

Machine learning and the physical sciences

(2019) Giuseppe Carleo et al.   REVIEWS OF MODERN PHYSICS

A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning

(2018) Kun Wang et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A partitioned coupling framework for peridynamics and classical theory: Analysis and simulations

(2018) Yue Yu et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A machine learning approach for efficient uncertainty quantification using multiscale methods

(2018) Shing Chan et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Modeling Soft Tissue Damage and Failure Using a Combined Particle/Continuum Approach

(2016) M. K. Rausch et al.   Biomechanics and Modeling in Mechanobiology

An hourglass control algorithm for Lagrangian Smooth Particle Hydrodynamics

(2015) Georg C. Ganzenmüller   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Fracture of graphene: a review

(2015) Teng Zhang et al.   INTERNATIONAL JOURNAL OF FRACTURE

Generalized multiscale finite element methods (GMsFEM)

(2013) Yalchin Efendiev et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Effect of defects on Young's modulus of graphene sheets: a molecular dynamics simulation

(2012) Nuannuan Jing et al.   RSC Advances

Stability, Instability, and Error of the Force-based Quasicontinuum Approximation

(2009) Matthew Dobson et al.   ARCHIVE FOR RATIONAL MECHANICS AND ANALYSIS

Equation-Free Multiscale Computation: Algorithms and Applications

(2008) Ioannis G. Kevrekidis et al.   Annual Review of Physical Chemistry