Operator learning for predicting multiscale bubble growth dynamics

Multiscale Modeling Meets Machine Learning: What Can We Learn?

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

Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

(2020) Maziar Raissi et al.   SCIENCE

Physics-informed neural networks for inverse problems in nano-optics and metamaterials

(2020) Yuyao Chen et al.   OPTICS EXPRESS

Deep model predictive flow control with limited sensor data and online learning

(2020) Katharina Bieker et al.   THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS

SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems

(2020) Pengzhan Jin et al.   NEURAL NETWORKS

Self-cleaning of hydrophobic rough surfaces by coalescence-induced wetting transition

(2019) Kaixuan Zhang et al.   LANGMUIR

Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates

(2019) Corentin J. Lapeyre et al.   COMBUSTION AND FLAME

Deep Neural Networks as Scientific Models

(2019) Radoslaw M. Cichy et al.   TRENDS IN COGNITIVE SCIENCES

Data-driven discovery of coordinates and governing equations

(2019) Kathleen Champion et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Physics-informed neural networks for high-speed flows

(2019) Zhiping Mao et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Machine learning and the physical sciences

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

A dissipative particle dynamics method for arbitrarily complex geometries

(2018) Zhen Li et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Tuning Drop Motion by Chemical Chessboard-Patterned Surfaces: A Many-Body Dissipative Particle Dynamics Study

(2018) Chensen Lin et al.   LANGMUIR

tempoGAN

(2018) You Xie et al.   ACM TRANSACTIONS ON GRAPHICS

Bubble Dynamics in Soft and Biological Matter

(2018) Benjamin Dollet et al.   Annual Review of Fluid Mechanics

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

Sliding Dynamic Behavior of a Nanobubble on a Surface

(2017) Cyuan-Jhang Wu et al.   Journal of Physical Chemistry C

High-frequency linear rheology of hydrogels probed by ultrasound-driven microbubble dynamics

(2017) Akaki Jamburidze et al.   Soft Matter

Data-driven discovery of partial differential equations

(2017) Samuel H. Rudy et al.   Science Advances

Rheology of bubble suspensions using dissipative particle dynamics. Part I: A hard-core DPD particle model for gas bubbles

(2013) Thien Tran-Duc et al.   JOURNAL OF RHEOLOGY

Three dimensional flow structures in a moving droplet on substrate: A dissipative particle dynamics study

(2013) Zhen Li et al.   PHYSICS OF FLUIDS

Continuum- and particle-based modeling of shapes and dynamics of red blood cells in health and disease

(2012) Xuejin Li et al.   Soft Matter

Many-body dissipative particle dynamics simulation of liquid/vapor and liquid/solid interactions

(2011) Marco Arienti et al.   JOURNAL OF CHEMICAL PHYSICS

VORO++: A three-dimensional Voronoi cell library in C++

(2009) Chris H. Rycroft   CHAOS