Search Results for author:
Found 6 papers, 4 papers with code
Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport
Deep neural operators can learn operators mapping between infinite-dimensional function spaces via deep neural networks and have become an emerging paradigm of scientific machine learning.
Nano-Material Configuration Design with Deep Surrogate Langevin Dynamics
We consider the problem of optimizing by sampling under multiple black-box constraints in nano-material design.
Phonon Transport in Patterned Two-Dimensional Materials from First Principles
Phonon size effects induce ballistic transport in nanomaterials, challenging Fourier's law.
Mesoscale and Nanoscale Physics Materials Science
Surrogate-Based Constrained Langevin Sampling With Applications to Optimal Material Configuration Design
We consider the problem of generating configurations that satisfy physical constraints for optimal material nano-pattern design, where multiple (and often conflicting) properties need to be simultaneously satisfied.
Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks
We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data.
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials.
